Mathematics Books

Book SynopsisDiscover the benefits of applying algorithms to solve scientific, engineering, and practical problems Providing a combination of theory, algorithms, and simulations, Handbook of Applied Algorithms presents an all-encompassing treatment of applying algorithms and discrete mathematics to practical problems in hot application areas, such as computational biology, computational chemistry, wireless networks, and computer vision. In eighteen self-contained chapters, this timely book explores: * Localized algorithms that can be used in topology control for wireless ad-hoc or sensor networks * Bioinformatics algorithms for analyzing data * Clustering algorithms and identification of association rules in data mining * Applications of combinatorial algorithms and graph theory in chemistry and molecular biology * Optimizing the frequency planning of a GSM network using evolutioTable of ContentsPreface. Abstracts. Contributors. 1. Generating All and Random Instances of A combinatorial Object (Ivan Stojmenovic) 2. Backtracking and Isomorph-Free Generation of Polyhexes (Lucia Moura and Ivan Stojmenovic) 3. Graph Theoretic Models in Chemistry and Molecular Biology (Debra Knisley and Jeff Knisley) 4. Algorithmic Methods for the Analysis of Gene Expression Data (Hongbo Xie, Uros Midic, Slobodan Vucetic, and Zoran Obradovic) 5. Algorithms of Reaction-Diffusion Computing (Andrew Adamatzky) 6. Data Mining Algorithms I: Clustering (Dan A. Simovici) 7. Data Mining Algorithms II: Frequent Item Sets (Dan A. Simovici) 8. Algorithms for Data Streams (Camil Demetrescu and Irene Finocchi) 9. Applying Evolutionary Algorithms to Solve the Automatic Frequency Planning Problem (Francisco Luna, Enrique Alba, Antonio J. Nero, Patrick Nauru, and Salvador Pedraza) 10. Algorithmic Game Theory and Application s(Marios Mavronicolas, Vicky Papdopoulou, and Paul Spirakis) 11. Algorithms for Real-Time Object Detection in Images (Milos Stojmenovic) 12. 2D Shape Measures for Computer Vision (Paul L. Rosin and Jovisa Zunic) 13. Cryptographic Algorithms (Binal Roy and Amiya Nayak) 14. Secure Communication in Distributed Sensor Networks (DSN) (Subhamoy Maitra and Bimal Roy) 15. Localized Topology Control Algorithms for Ad Hoc and Sensor Networks (Hannes Frey and David Simplot-Ryl) 16. A Novel Admission Control for Multimedia LEO Satellite Networks (Syed R. Rizvi, Stephan Olariu, and Mona E. Rizvi) 17. Resilient Recursive Routing in Communication Networks (Costas C. Constantinou, Alexander S. Stepanenko, Theodoros N. Arvanitis, Kevin J. Baughan, and Bin Liu) 18. Routing Algorithms on WDM Optical Networks (Qian-Ping Gu) Index.

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Book SynopsisA balanced introduction to the theoretical foundations and real-world applications of mathematical finance The ever-growing use of derivative products makes it essential for financial industry practitioners to have a solid understanding of derivative pricing. To cope with the growing complexity, narrowing margins, and shortening life-cycle of the individual derivative product, an efficient, yet modular, implementation of the pricing algorithms is necessary. Mathematical Finance is the first book to harmonize the theory, modeling, and implementation of today''s most prevalent pricing models under one convenient cover. Building a bridge from academia to practice, this self-contained text applies theoretical concepts to real-world examples and introduces state-of-the-art, object-oriented programming techniques that equip the reader with the conceptual and illustrative tools needed to understand and develop successful derivative pricing models. Utilizing almost tweTrade Review"…very useful to practitioners and students…" (MAA Reviews, December 26, 2007) "An excellent textbook for students in mathematical finance, computational finance, and derivative pricing courses at the upper undergraduate or beginning graduate level." (Mathematical Reviews 2007)Table of Contents1. Introduction. 1.1 Theory, Modeling and Implementation. 1.2 Interest Rate Models and Interest Rate Derivatives. 1.3 How to Read this Book. 1.3.1 Abridged Versions. 1.3.2 Special Sections. 1.3.3 Notation. I: FOUNDATIONS. 2. Foundations. 2.1 Probability Theory. 2.2 Stochastic Processes. 2.3 Filtration. 2.4 Brownian Motion. 2.5 Wiener Measure, Canonical Setup. 2.6 Itô Calculus. 2.6.1 Itô Integral. 2.6.2 Itô Process. 2.6.3 Itô Lemma and Product Rule. 2.7 Brownian Motion with Instantaneous Correlation. 2.8 Martingales. 2.8.1 Martingale Representation Theorem. 2.9 Change of Measure (Girsanov, Cameron, Martin). 2.10 Stochastic Integration. 2.11 Partial Differential Equations (PDE). 2.11.1 Feynman-Kac Theorem . 2.12 List of Symbols. 3. Replication. 3.1 Replication Strategies. 3.1.1 Introduction. 3.1.2 Replication in a discrete Model. 3.2 Foundations: Equivalent Martingale Measure. 3.2.1 Challenge and Solution Outline. 3.2.2 Steps towards the Universal Pricing Theorem. 3.3 Excursus: Relative Prices and Risk Neutral Measures. 3.3.1 Why relative prices? 3.3.2 Risk Neutral Measure. II: FIRST APPLICATIONS. 4. Pricing of a European Stock Option under the Black-Scholes Model. 5. Excursus: The Density of the Underlying of a European Call Option. 6. Excursus: Interpolation of European Option Prices. 6.1 No-Arbitrage Conditions for Interpolated Prices. 6.2 Arbitrage Violations through Interpolation. 6.2.1 Example (1): Interpolation of four Prices. 6.2.2 Example (2): Interpolation of two Prices. 6.3 Arbitrage-Free Interpolation of European Option Prices. 7. Hedging in Continuous and Discrete Time and the Greeks. 7.1 Introduction. 7.2 Deriving the Replications Strategy from Pricing Theory. 7.2.1 Deriving the Replication Strategy under the Assumption of a Locally Riskless Product. 7.2.2 The Black-Scholes Differential Equation. 7.2.3 The Derivative V(t) as a Function of its Underlyings S i(t). 7.2.4 Example: Replication Portfolio and PDE under a Black-Scholes Model. 7.3 Greeks. 7.3.1 Greeks of a European Call-Option under the Black-Scholes model. 7.4 Hedging in Discrete Time: Delta and Delta-Gamma Hedging. 7.4.1 Delta Hedging. 7.4.2 Error Propagation. 7.4.3 Delta-Gamma Hedging. 7.4.4 Vega Hedging. 7.5 Hedging in Discrete Time: Minimizing the Residual Error (Bouchaud-Sornette Method). 7.5.1 Minimizing the Residual Error at Maturity T. 7.5.2 Minimizing the Residual Error in each Time Step. III: INTEREST RATE STRUCTURES, INTEREST RATE PRODUCTS AND ANALYTIC PRICING FORMULAS. Motivation and Overview. 8. Interest Rate Structures. 8.1 Introduction. 8.1.1 Fixing Times and Tenor Times. 8.2 Definitions. 8.3 Interest Rate Curve Bootstrapping. 8.4 Interpolation of Interest Rate Curves. 8.5 Implementation. 9. Simple Interest Rate Products. 9.1 Interest Rate Products Part 1: Products without Optionality. 9.1.1 Fix, Floating and Swap. 9.1.2 Money-Market Account. 9.2 Interest Rate Products Part 2: Simple Options. 9.2.1 Cap, Floor, Swaption. 9.2.2 Foreign Caplet, Quanto. 10. The Black Model for a Caplet. 11. Pricing of a Quanto Caplet (Modeling the FFX). 11.1 Choice of Numéraire. 12. Exotic Derivatives. 12.1 Prototypical Product Properties. 12.2 Interest Rate Products Part 3: Exotic Interest Rate Derivatives. 12.2.1 Structured Bond, Structured Swap, Zero Structure. 12.2.2 Bermudan Option. 12.2.3 Bermudan Callable and Bermudan Cancelable. 12.2.4 Compound Options. 12.2.5 Trigger Products. 12.2.6 Structured Coupons. 12.2.7 Shout Options. 12.3 Product Toolbox. IV: DISCRETIZATION AND NUMERICAL VALUATION METHODS. Motivation and Overview. 13. Discretization of time and state space. 13.1 Discretization of Time: The Euler and the Milstein Scheme. 13.1.1 Definitions. 13.1.2 Time-Discretization of a Lognormal Process. 13.2 Discretization of Paths (Monte-Carlo Simulation) . 13.2.1 Monte-Carlo Simulation. 13.2.2 Weighted Monte-Carlo Simulation. 13.2.3 Implementation. 13.2.4 Review. 13.3 Discretization of State Space. 13.3.1 Definitions. 13.3.2 Backward-Algorithm. 13.3.3 Review. 13.4 Path Simulation through a Lattice: Two Layers. 14. Numerical Methods for Partial Differential Equations. 15. Pricing Bermudan Options in a Monte Carlo Simulation. 15.1 Introduction. 15.2 Bermudan Options: Notation. 15.2.1 Bermudan Callable. 15.2.2 Relative Prices. 15.3 Bermudan Option as Optimal Exercise Problem. 15.3.1 Bermudan Option Value as single (unconditioned) Expectation: The Optimal Exercise Value. 15.4 Bermudan Option Pricing - The Backward Algorithm. 15.5 Re-simulation. 15.6 Perfect Foresight. 15.7 Conditional Expectation as Functional Dependence. 15.8 Binning. 15.8.1 Binning as a Least-Square Regression. 15.9 Foresight Bias. 15.10 Regression Methods - Least Square Monte-Carlo. 15.10.1 Least Square Approximation of the Conditional Expectation. 15.10.2 Example: Evaluation of a Bermudan Option on a Stock (Backward Algorithm with Conditional Expectation Estimator). 15.10.3 Example: Evaluation of a Bermudan Callable. 15.10.4 Implementation. 15.10.5 Binning as linear Least-Square Regression. 15.11 Optimization Methods. 15.11.1 Andersen Algorithm for Bermudan Swaptions. 15.11.2 Review of the Threshold Optimization Method. 15.11.3 Optimization of Exercise Strategy: A more general Formulation. 15.11.4 Comparison of Optimization Method and Regression. Method. 15.12 Duality Method: Upper Bound for Bermudan Option Prices. 15.12.1 Foundations. 15.12.2 American Option Evaluation as Optimal Stopping Problem. 15.13 Primal-Dual Method: Upper and Lower Bound. 16. Pricing Path-Dependent Options in a Backward Algorithm. 16.1 Evaluation of a Snowball / Memory in a Backward Algorithm. 16.2 Evaluation of a Flexi Cap in a Backward Algorithm. 17. Sensitivities (Partial Derivatives) of Monte Carlo Prices. 17.1 Introduction. 17.2 Problem Description. 17.2.1 Pricing using Monte-Carlo Simulation. 17.2.2 Sensitivities from Monte-Carlo Pricing. 17.2.3 Example: The Linear and the Discontinuous Payout. 17.2.4 Example: Trigger Products. 17.3 Generic Sensitivities: Bumping the Model. 17.4 Sensitivities by Finite Differences. 17.4.1 Example: Finite Differences applied to Smooth and Discontinuous Payout. 17.5 Sensitivities by Pathwise Differentiation. 17.5.1 Example: Delta of a European Option under a Black-Scholes Model. 17.5.2 Pathwise Differentiation for Discontinuous Payouts. 17.6 Sensitivities by Likelihood Ratio Weighting. 17.6.1 Example: Delta of a European Option under a Black-Scholes Model using Pathwise Derivative. 17.6.2 Example: Variance Increase of the Sensitivity when using Likelihood Ratio Method for Smooth Payouts. 17.7 Sensitivities by Malliavin Weighting. 17.8 Proxy Simulation Scheme. 18. Proxy Simulation Schemes for Monte Carlo Sensitivities and Importance Sampling. 18.1 Full Proxy Simulation Scheme. 18.1.1 Calculation of Monte-Carlo weights. 18.2 Sensitivities by Finite Differences on a Proxy Simulation Scheme. 18.2.1 Localization. 18.2.2 Object-Oriented Design. 18.3 Importance Sampling. 18.3.1 Example. 18.4 Partial Proxy Simulation Schemes. 18.4.1 Linear Proxy Constraint. 18.4.2 Comparison to Full Proxy Scheme Method. 18.4.3 Non-Linear Proxy Constraint. 18.4.4 Transition Probability from a Nonlinear Proxy Constraint. 18.4.5 Sensitivity with respect to the Diffusion Coefficients - Vega. 18.4.6 Example: LIBOR Target Redemption Note. 18.4.7 Example: CMS Target Redemption Note. V: PRICING MODELS FOR INTEREST RATE DERIVATIVES. 19. LIBOR Market Models. 19.1 LIBOR Market Model. 19.1.1 Derivation of the Drift Term. 19.1.2 The Short Period Bond P(Tm(t)+1;t) . 19.1.3 Discretization and (Monte-Carlo) Simulation. 19.1.4 Calibration - Choice of the free Parameters. 19.1.5 Interpolation of Forward Rates in the LIBOR Market Model. 19.2 Object Oriented Design. 19.2.1 Reuse of Implementation. 19.2.2 Separation of Product and Model. 19.2.3 Abstraction of Model Parameters. 19.2.4 Abstraction of Calibration. 19.3 Swap Rate Market Models (Jamshidian 1997). 19.3.1 The Swap Measure. 19.3.2 Derivation of the Drift Term. 19.3.3 Calibration - Choice of the free Parameters. 20. Swap Rate Market Models. 20.1 Definitions. 20.2 Terminal Correlation examined in a LIBOR Market Model Example. 20.2.1 De-correlation in a One-Factor Model. 20.2.2 Impact of the Time Structure of the Instantaneous Volatility on Caplet and Swaption Prices. 20.2.3 The Swaption Value as a Function of Forward Rates. 20.3 Terminal Correlation is dependent on the Equivalent Martingale Measure. 20.3.1 Dependence of the Terminal Density on the Martingale Measure. 21. Excursus: Instantaneous Correlation and Terminal Correlation. 21.1 Short Rate Process in the HJM Framework. 21.2 The HJM Drift Condition. 22.Heath-Jarrow-Morton Framework: Foundations. 22.1 Introduction. 22.2 The Market Price of Risk. 22.3 Overview: Some Common Models. 22.4 Implementations. 22.4.1 Monte-Carlo Implementation of Short-Rate Models. 22.4.2 Lattice Implementation of Short-Rate Models. 23. Short-Rate Models. 23.1 Short Rate Models in the HJM Framework. 23.1.1 Example: The Ho-Lee Model in the HJM Framework. 23.1.2 Example: The Hull-White Model in the HJM Framework. 23.2 LIBOR Market Model in the HJM Framework. 23.2.1 HJM Volatility Structure of the LIBOR Market Model. 23.2.2 LIBOR Market Model Drift under the QB Measure. 23.2.3 LIBOR Market Model as a Short Rate Model. 24 Heath-Jarrow-Morton Framwork: Immersion of Short-Rate Models and LIBOR Market Model. 24.1 Model. 24.2 Interpretation of the Figures. 24.3 Mean Reversion. 24.4 Factors. 24.5 Exponential Volatility Function. 24.6 Instantaneous Correlation. 25. Excursus: Shape of teh Interst Rate Curve under Mean Reversion and a Multifactor Model. 25.1 Introduction. 25.2 Cheyette Model. 26. Ritchken-Sakarasubramanian Framework: JHM with Low Markov Dimension. 26.1 Introduction. 26.1.1 The Markov Functional Assumption (independent of the model considered) . 26.1.2 Outline of this Chapter . 26.2 Equity Markov Functional Model. 26.2.1 Markov Functional Assumption. 26.2.2 Example: The Black-Scholes Model. 26.2.3 Numerical Calibration to a Full Two-Dimensional European Option Smile Surface. 26.2.4 Interest Rates. 26.2.5 Model Dynamics. 26.2.6 Implementation. 26.3 LIBOR Markov Functional Model. 26.3.1 LIBOR Markov Functional Model in Terminal Measure. 26.3.2 LIBOR Markov Functional Model in Spot Measure. 26.3.3 Remark on Implementation. 26.3.4 Change of numéraire in a Markov-Functional Model. 26.4 Implementation: Lattice. 26.4.1 Convolution with the Normal Probability Density. 26.4.2 State space discretization. Markov Functional Models. PART VI: Extended Models. 27.1 Introduction - Different Types of Spreads. 27.1.1 Spread on a Coupon. 27.1.2 Credit Spread. 27.2 Defaultable Bonds. 27.3 Integrating deterministic Credit Spread into a Pricing Model. 27.3.1 Deterministic Credit Spread. 27.3.2 Implementation. 27.4 Receiver’s and Payer’s Credit Spreads. 27.4.1 Example: Defaultable Forward Starting Coupon Bond. 27.4.2 Example: Option on a Defaultable Coupon Bond. 28. Credit Spreads. 28.1 Cross Currency LIBOR Market Model. 28.1.1 Derivation of the Drift Term under Spot-Measure. 28.1.2 Implementation. 28.2 Equity Hybrid LIBOR Market Model. 28.2.1 Derivation of the Drift Term under Spot-Measure. 28.2.2 Implementation. 28.3 Equity-Hybrid Cross-Currency LIBOR Market Model. 28.3.1 Summary. 28.3.2 Implementation. 29. Hybrid Models. 29.1 Elements of Object Oriented Programming: Class and Objects. 29.1.1 Example: Class of a Binomial Distributed Random Variable. 29.1.2 Constructor. 29.1.3 Methods: Getter, Setter, Static Methods. 29.2 Principles of Object Oriented Programming. 29.2.1 Encapsulation and Interfaces. 29.2.2 Abstraction and Inheritance. 29.2.3 Polymorphism. 29.3 Example: A Class Structure for One Dimensional Root Finders. 29.3.1 Root Finder for General Functions. 29.3.2 Root Finder for Functions with Analytic Derivative: Newton Method. 29.3.3 Root Finder for Functions with Derivative Estimation: Secant Method. 29.4 Anatomy of a Java™ Class. 29.5 Libraries. 29.5.1 Java™2 Platform, Standard Edition (j2se). 29.5.2 Java™2 Platform, Enterprise Edition (j2ee). 29.5.3 Colt. 29.5.4 Commons-Math: The Jakarta Mathematics Library. 29.6 Some Final Remarks. 29.6.1 Object Oriented Design (OOD) / Unified Modeling Language. PART VII: Implementation 30. Object-Oriented Implementatin in JavaTM. PART VIII: Appendices. A: A small Collection of Common Misconceptions. B: Tools (Selection). B.1 Linear Regression. B.2 Generation of Random Numbers. B.2.1 Uniform Distributed Random Variables. B.2.2 Transformation of the Random Number Distribution via the Inverse Distribution Function. B.2.3 Normal Distributed Random Variables. B.2.4 Poisson Distributed Random Variables. B.2.5 Generation of Paths of an n-dimensional Brownian Motion. B.3 Factor Decomposition - Generation of Correlated Brownian Motion. B.4 Factor Reduction. B.5 Optimization (one-dimensional): Golden Section Search. B.6 Convolution with Normal Density. C: Exercises. D: List of Symbols. E: Java™ Source Code (Selection). E.1 Java™ Classes for Chapter 29. List of Figures. List of Tables. List of Listings. Bibliography. Index.

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Book SynopsisThe Construction of Optimal Stated Choice Experiments provides an accessible introduction to the construction methods needed to create the best possible designs for use in modeling decision-making. It uniquely covers disciplines from marketing to transportation, environmental resource economics to public welfare analysis.Trade Review"Individuals involved in researching, learning about, or using SCE's are most likely to find this book useful." (Biometrics, September 2008) "Individuals involved in researching, learning about, or using SCE's are most likely to find this book useful." (Biometrics, Sept 2008) "This book provides an excellent treatment of structure of D-optimal designs for choice experiments. I enjoyed reading it.." (Journal of the American Statistical Association, Sept 2008)Table of ContentsList of Tables. Preface. 1. Typical Stated Choice Experiments. 2. Factorial Designs. 3. The MNL Model and Comparing Designs. 4. Paired Comparison Designs for Binary Attributes. 5. Larger Choice Set Sizes for Binary Attributes. 6. Designs for Asymmetric Attributes. 7. Various Topics. 8. Practical Techniques For Constructing Choice Experiments. Bibliography. Index.

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Book SynopsisThe authority on building empirical models and the fitting of such surfaces to datacompletely updated and revised Revising and updating a volume that represents the essential source on building empirical models, George Box and Norman Draperrenowned authorities in this fieldcontinue to set the standard with the Second Edition of Response Surfaces, Mixtures, and Ridge Analyses, providing timely new techniques, new exercises, and expanded material. A comprehensive introduction to building empirical models, this book presents the general philosophy and computational details of a number of important topics, including factorial designs at two levels; fitting first and second-order models; adequacy of estimation and the use of transformation; and occurrence and elucidation of ridge systems. Substantially rewritten, the Second Edition reflects the emergence of ridge analysis of second-order response surfaces as a very practical tool that can be easily applied in a variety of Trade Review“The book can be used as a primary text for graduate courses in response surface methodology, as a supplementary text for advanced undergraduate and beginning graduate courses in design of experiments, and as a reference for researchers.” (SciTech Book Reviews, June 2007) "This book should be mandatory on the shelves of all statisticians. If you own the first edition, it is time to update it with this latest version." (Journal of the American Statistical Association, June 2008) "…an important reference source." (International Statistical Review, 2007)Table of ContentsPreface to the Second Edition. Chapter 1. Introduction to response Surface Methodology. Chapter 2. The Use of Graduating Functions. Chapter 3. Least Squares for Response Surface Work. Chapter 4. Factorial Designs at Two Levels. Chapter 5. Blocking and Fractionating 2k Factorial Designs. Chapter 6. The Use of Steepest Ascent to Achieve Process Improvement. Chapter 7. Fitting Second-Order Models. Chapter 8. Adequacy of Estimation and the Use of Transformation. Chapter 9. Exploration of Maxima and Ridge Systems with Second-Oder Response Surfaces. Chapter 10. Occurrence and Elucidation of Ridge Systems, I. Chapter 11. Occurrence and Elucidation of Ridge Systems, II. Chapter 12. Ridge Analysis for Examining Second-Order Fitted Models, Unrestricted Case. Chapter 13. Design Aspects of Variance, Bias, and Lack of Fit. Chapter 14. Variance-Optimal Designs. Chapter 15. Practical Choice of a Response Surface Design. Chapter 16. Response Surfaces for Mixture Ingredients. Chapter 17. Mixture Experiments in Restricted Regions. Chapter 18. Other Mixture Methods and Topics. Chapter 19. Ridge Analysis for Examining Second-Order Fitted Models when there Are Linear Restrictions on the Experimental region. Chapter 20. Canonical Reduction of Second-Order Fitted Models Subject to Linear Restrictions. Answers to Exercises. Tables. Bibliography. Author Index. Subject Index.

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Book SynopsisOur understanding of the fundamental processes of the natural world is based to a large extent on partial differential equations (PDEs). The second edition of Partial Differential Equations provides an introduction to the basic properties of PDEs and the ideas and techniques that have proven useful in analyzing them. It provides the student a broad perspective on the subject, illustrates the incredibly rich variety of phenomena encompassed by it, and imparts a working knowledge of the most important techniques of analysis of the solutions of the equations. In this book mathematical jargon is minimized. Our focus is on the three most classical PDEs: the wave, heat and Laplace equations. Advanced concepts are introduced frequently but with the least possible technicalities. The book is flexibly designed for juniors, seniors or beginning graduate students in science, engineering or mathematics.Table of ContentsChapter 1/Where PDEs Come From 1.1* What is a Partial Differential Equation? 1 1.2* First-Order Linear Equations 6 1.3* Flows, Vibrations, and Diffusions 10 1.4* Initial and Boundary Conditions 20 1.5 Well-Posed Problems 25 1.6 Types of Second-Order Equations 28 Chapter 2/Waves and Diffusions 2.1* The Wave Equation 33 2.2* Causality and Energy 39 2.3* The Diffusion Equation 42 2.4* Diffusion on the Whole Line 46 2.5* Comparison of Waves and Diffusions 54 Chapter 3/Reflections and Sources 3.1 Diffusion on the Half-Line 57 3.2 Reflections of Waves 61 3.3 Diffusion with a Source 67 3.4 Waves with a Source 71 3.5 Diffusion Revisited 80 Chapter 4/Boundary Problems 4.1* Separation of Variables, The Dirichlet Condition 84 4.2* The Neumann Condition 89 4.3* The Robin Condition 92 Chapter 5/Fourier Series 5.1* The Coefficients 104 5.2* Even, Odd, Periodic, and Complex Functions 113 5.3* Orthogonality and General Fourier Series 118 5.4* Completeness 124 5.5 Completeness and the Gibbs Phenomenon 136 5.6 Inhomogeneous Boundary Conditions 147 Chapter 6/Harmonic Functions 6.1* Laplace’s Equation 152 6.2* Rectangles and Cubes 161 6.3* Poisson’s Formula 165 6.4 Circles, Wedges, and Annuli 172 Chapter 7/Green’s Identities and Green’s Functions 7.1 Green’s First Identity 178 7.2 Green’s Second Identity 185 7.3 Green’s Functions 188 7.4 Half-Space and Sphere 191 Chapter 8/Computation of Solutions 8.1 Opportunities and Dangers 199 8.2 Approximations of Diffusions 203 8.3 Approximations of Waves 211 8.4 Approximations of Laplace’s Equation 218 8.5 Finite Element Method 222 Chapter 9/Waves in Space 9.1 Energy and Causality 228 9.2 The Wave Equation in Space-Time 234 9.3 Rays, Singularities, and Sources 242 9.4 The Diffusion and Schrodinger Equations 248 ¨ 9.5 The Hydrogen Atom 254 Chapter 10/Boundaries in the Plane and in Space 10.1 Fourier’s Method, Revisited 258 10.2 Vibrations of a Drumhead 264 10.3 Solid Vibrations in a Ball 270 10.4 Nodes 278 10.5 Bessel Functions 282 10.6 Legendre Functions 289 10.7 Angular Momentum in Quantum Mechanics 294 Chapter 11/General Eigenvalue Problems 11.1 The Eigenvalues Are Minima of the Potential Energy 299 11.2 Computation of Eigenvalues 304 11.3 Completeness 310 11.4 Symmetric Differential Operators 314 11.5 Completeness and Separation of Variables 318 11.6 Asymptotics of the Eigenvalues 322 Chapter 12/Distributions and Transforms 12.1 Distributions 331 12.2 Green’s Functions, Revisited 338 12.3 Fourier Transforms 343 12.4 Source Functions 349 12.5 Laplace Transform Techniques 353 Chapter 13/PDE Problems from Physics 13.1 Electromagnetism 358 13.2 Fluids and Acoustics 361 13.3 Scattering 366 13.4 Continuous Spectrum 370 13.5 Equations of Elementary Particles 373 Chapter 14/Nonlinear PDEs 14.1 Shock Waves 380 14.2 Solitons 390 14.3 Calculus of Variations 397 14.4 Bifurcation Theory 401 14.5 Water Waves 406 Appendix A.1 Continuous and Differentiable Functions 414 A.2 Infinite Series of Functions 418 A.3 Differentiation and Integration 420 A.4 Differential Equations 423 A.5 The Gamma Function 425 References 427 Answers and Hints to Selected Exercises 431 Index 446

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Book SynopsisA unique and invaluable reference resource for those working in survival analysis. Survival analysis is concerned with studying the time between entry to a study and a subsequent event.Trade Review"This volume is recommended to libraries, a welcome addition…" (Technometrics, August 2007)Table of ContentsList of Contributors. Series Preface. Preface. List of Abbreviations and Acronyms. Aalen-Johansen Estimator. Aalen's Additive Regression Model. Accelerated Failure-time Models. Actuarial Methods. Additive Hazard Models. Additive-Multiplicative Intensity Models. Aging Models. Age-of-onset Estimation. Age-Period-Cohort Analysis. Bayesian Approaches to Cure Rate Models. Bayesian Model Selection in Survival Analysis. Bayesian Survival Analysis. Bootstrapping in Survival Analysis. Case-Cohort Study. Case-Control Study, Nested. Censored Data. Coarsening at Random. Competing Risks. Compliance and Survival Analysis. Copula. Counting Process Methods in Survival Analysis. Cox Regression Model. Cure Models. Delayed Entry. Discrete Survival-time models. Duration Dependence. Event History Analysis. Excess Mortality. Expected Number of Deaths. Expected Survival Curve. Explained Variation Measures in Survival Analysis. Exponential Distribution. Frailty. Ghosts. Goodness of Fit in Survival Analysis. Grenander Estimators. Grouped Survival Times. Hazard Models Based on First-passage Time Distributions. Hazard Plotting. Hazard Ratio Estimator. Historical Controls in Survival Analysis. Incidence-Prevalence Relationships. Influence Function in Survival Analysis. Interim Analysis of Censored Data. Interval Censoring. Inverse Probability Weighting in Survival Analysis. Joint Modeling of Longitudinal and Event Time Data. Kaplan-Meier Estimator. Kolmogorov-Smirnov and Cramer-Von Mises Tests in Survival Analysis. Length Bias. Lexis Diagram. Life Table. Linear Rank Tests in Survival Analysis. Logrank Test. Marginal Models for Multivariate Survival Data. Marker Processes. Measurement Error in Survival Analysis. Median Survival Time. Multivariate Survival Analysis. Nelson-Aalen Estimator. Nonparametric Maximum Likelihood. Parametric Models in Survival Analysis. Pareto Distribution. Partial Likelihood. Phase-type Distributions in Survival Analysis. Poisson Regression in Epidemiology. Product-integration. Prognostic Factors for Survival . Proportional Hazards, Overview. Proportional-odds Regression. Quality of life and Survival Analysis. Rank Regression. Real Time Approach in Survival Analysis. Relative Risk Modeling. Repeated Events. Residuals for Survival Analysis. Risk Set. Sample Size Determination in Survival Analysis. Semiparametric Regression. Smoothing Hazard Rates. Staggered Entry. Standardization Methods. Structural Nested Failure Time Models. Surrogate Endpoints. Survival Analysis, Overview . Survival Analysis, Software. Survival Distributions and Their Characteristics. Tied Survival Times . Time Origin, Choice of. Time to Pregnancy. Time-dependent Covariate. Total Time on Test. Truncated Survival Times. Turnbull Estimator. Type-specific Covariates in Survival Analysis. Weibull Distribution. Author Index. Subject Index.

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Book SynopsisThe increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods. Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining. Features detailed case studies based on applied projects within industry. Trade Review“If I had to recommend a good introduction to data mining, I would choose this one.” (Stat Papers, 2011) Table of Contents1 Introduction. Part I Methodology. 2 Organisation of the data. 2.1 Statistical units and statistical variables. 2.2 Data matrices and their transformations. 2.3 Complex data structures. 2.4 Summary. 3 Summary statistics. 3.1 Univariate exploratory analysis. 3.1.1 Measures of location. 3.1.2 Measures of variability. 3.1.3 Measures of heterogeneity. 3.1.4 Measures of concentration. 3.1.5 Measures of asymmetry. 3.1.6 Measures of kurtosis. 3.2 Bivariate exploratory analysis of quantitative data. 3.3 Multivariate exploratory analysis of quantitative data. 3.4 Multivariate exploratory analysis of qualitative data. 3.4.1 Independence and association. 3.4.2 Distance measures. 3.4.3 Dependency measures. 3.4.4 Model-based measures. 3.5 Reduction of dimensionality. 3.5.1 Interpretation of the principal components. 3.6 Further reading. 4 Model specification. 4.1 Measures of distance. 4.1.1 Euclidean distance. 4.1.2 Similarity measures. 4.1.3 Multidimensional scaling. 4.2 Cluster analysis. 4.2.1 Hierarchical methods. 4.2.2 Evaluation of hierarchical methods. 4.2.3 Non-hierarchical methods. 4.3 Linear regression. 4.3.1 Bivariate linear regression. 4.3.2 Properties of the residuals. 4.3.3 Goodness of fit. 4.3.4 Multiple linear regression. 4.4 Logistic regression. 4.4.1 Interpretation of logistic regression. 4.4.2 Discriminant analysis. 4.5 Tree models. 4.5.1 Division criteria. 4.5.2 Pruning. 4.6 Neural networks. 4.6.1 Architecture of a neural network. 4.6.2 The multilayer perceptron. 4.6.3 Kohonen networks. 4.7 Nearest-neighbour models. 4.8 Local models. 4.8.1 Association rules. 4.8.2 Retrieval by content. 4.9 Uncertainty measures and inference. 4.9.1 Probability. 4.9.2 Statistical models. 4.9.3 Statistical inference. 4.10 Non-parametric modelling. 4.11 The normal linear model. 4.11.1 Main inferential results. 4.12 Generalised linear models. 4.12.1 The exponential family. 4.12.2 Definition of generalised linear models. 4.12.3 The logistic regression model. 4.13 Log-linear models. 4.13.1 Construction of a log-linear model. 4.13.2 Interpretation of a log-linear model. 4.13.3 Graphical log-linear models. 4.13.4 Log-linear model comparison. 4.14 Graphical models. 4.14.1 Symmetric graphical models. 4.14.2 Recursive graphical models. 4.14.3 Graphical models and neural networks. 4.15 Survival analysis models. 4.16 Further reading. 5 Model evaluation. 5.1 Criteria based on statistical tests. 5.1.1 Distance between statistical models. 5.1.2 Discrepancy of a statistical model. 5.1.3 Kullback–Leibler discrepancy. 5.2 Criteria based on scoring functions. 5.3 Bayesian criteria. 5.4 Computational criteria. 5.5 Criteria based on loss functions. 5.6 Further reading. Part II Business case studies. 6 Describing website visitors. 6.1 Objectives of the analysis. 6.2 Description of the data. 6.3 Exploratory analysis. 6.4 Model building. 6.4.1 Cluster analysis. 6.4.2 Kohonen networks. 6.5 Model comparison. 6.6 Summary report. 7 Market basket analysis. 7.1 Objectives of the analysis. 7.2 Description of the data. 7.3 Exploratory data analysis. 7.4 Model building. 7.4.1 Log-linear models. 7.4.2 Association rules. 7.5 Model comparison. 7.6 Summary report. 8 Describing customer satisfaction. 8.1 Objectives of the analysis. 8.2 Description of the data. 8.3 Exploratory data analysis. 8.4 Model building. 8.5 Summary. 9 Predicting credit risk of small businesses. 9.1 Objectives of the analysis. 9.2 Description of the data. 9.3 Exploratory data analysis. 9.4 Model building. 9.5 Model comparison. 9.6 Summary report. 10 Predicting e-learning student performance. 10.1 Objectives of the analysis. 10.2 Description of the data. 10.3 Exploratory data analysis. 10.4 Model specification. 10.5 Model comparison. 10.6 Summary report. 11 Predicting customer lifetime value. 11.1 Objectives of the analysis. 11.2 Description of the data. 11.3 Exploratory data analysis. 11.4 Model specification. 11.5 Model comparison. 11.6 Summary report. 12 Operational risk management. 12.1 Context and objectives of the analysis. 12.2 Exploratory data analysis. 12.3 Model building. 12.4 Model comparison. 12.5 Summary conclusions. References. Index.

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Book SynopsisThis guide offers an overview of the most common types of trial undertaken in early clinical development. The coverage discusses the different methodologies and the impact of new technologies, both clinical and statistical, on clinical development.Trade Review"An Introduction to Statistics in Early Phase Trials" is an admirably concise and practical guide to the pertinent context, principles and formulae for statisticians inexpert in the application of their discipline to Phase I and II clinical research". (Journal of Clinical Research Best Practices, 1 March 2011) “An Introduction to Statistics in Early Phase Trials provides concise descriptions of many early phase trial designs, along with the statistical equations necessary to gather and analyze the data” (Annals of Pharmacotherapy, 2010) "I enjoyed reading the work of Dr. Julious, Tan, and Machin,found it quite useful, and recommend it to others teaching about, working with, or considering work in the learning phase of drug development." (Journal of Biopharmaceutical Statistics, 2011) Table of ContentsChapter 1 Early phase trials 1 Chapter 2 Introduction to pharmacokinetics 13 Chapter 3 Sample size calculations for clinical trials 37 Chapter 4 Crossover trial basics 55 Chapter 5 Multi-period crossover trials 71 Chapter 6 First time into man 87 Chapter 7 Bayesian and frequentist methods 113 Chapter 8 First-time-into-new-population studies 125 Chapter 9 Bioequivalence studies 139 Chapter 10 Other Phase I trials 169 Chapter 11 Phase II trials: general issues 187 Chapter 12 Dose–response studies 197 Chapter 13 Phase II trials with toxic therapies 211 Chapter 14 Interpreting and applying early phase trial results 223 Chapter 15 Go/No-Go criteria 231 Appendix 245 References 251 Index 257

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Book SynopsisWritten by the leading names in the field, Global Sensitivity Analysis: The Primer offers an accessible summary of the essential concepts involved in a sound sensitivity analysis. It is a self-contained book allowing the reader to learn about, and practice, sensitivity analysis through the use of many exercises and solved problems.Trade Review"This is one of the few books to take on the problem head on and provide techniques in a very simple way." (Technometrics, November 2008)Table of ContentsPreface. 1. Introduction to Sensitivity Analysi. 1.1 Models and Sensitivity Analysis. 1.1.1 Definition. 1.1.2 Models. 1.1.3 Models and Uncertainty. 1.1.4 How to Set Up Uncertainty and Sensitivity Analyses. 1.1.5 Implications for Model Quality. 1.2 Methods and Settings for Sensitivity Analysis - An Introduction. 1.2.1 Local versus Global. 1.2.2 A Test Model. 1.2.3 Scatterplots versus Derivatives. 1.2.4 Sigma-normalized Derivatives. 1.2.5 Monte Carlo and Linear Regression. 1.2.6 Conditional Variances - First Path. 1.2.7 Conditional Variances - Second Path. 1.2.8 Application to Model (1.3). 1.2.9 A First Setting: 'Factor Prioritization' 1.2.10 Nonadditive Models. 1.2.11 Higher-order Sensitivity Indices. 1.2.12 Total Effects. 1.2.13 A Second Setting: 'Factor Fixing'. 1.2.14 Rationale for Sensitivity Analysis. 1.2.15 Treating Sets. 1.2.16 Further Methods. 1.2.17 Elementary Effect Test. 1.2.18 Monte Carlo Filtering. 1.3 Nonindependent Input Factors. 1.4 Possible Pitfalls for a Sensitivity Analysis. 1.5 Concluding Remarks. 1.6 Exercises. 1.7 Answers. 1.8 Additional Exercises. 1.9 Solutions to Additional Exercises. 2. Experimental Designs. 2.1 Introduction. 2.2 Dependency on a Single Parameter. 2.3 Sensitivity Analysis of a Single Parameter. 2.3.1 Random Values. 2.3.2 Stratified Sampling. 2.3.3 Mean and Variance Estimates for Stratified Sampling. 2.4 Sensitivity Analysis of Multiple Parameters. 2.4.1 Linear Models. 2.4.2 One-at-a-time (OAT) Sampling. 2.4.3 Limits on the Number of Influential Parameters. 2.4.4 Fractional Factorial Sampling. 2.4.5 Latin Hypercube Sampling. 2.4.6 Multivariate Stratified Sampling. 2.4.7 Quasi-random Sampling with Low-discrepancy Sequences. 2.5 Group Sampling. 2.6 Exercises. 2.7 Exercise Solutions. 3. Elementary Effects Method. 3.1 Introduction. 3.2 The Elementary Effects Method. 3.3 The Sampling Strategy and its Optimization. 3.4 The Computation of the Sensitivity Measures. 3.5 Working with Groups. 3.6 The EE Method Step by Step. 3.7 Conclusions. 3.8 Exercises. 3.9 Solutions. 4. Variance-based Methods. 4.1 Different Tests for Different Settings. 4.2 Why Variance? 4.3 Variance-based Methods. A Brief History. 4.4 Interaction Effects. 4.5 Total Effects. 4.6 How to Compute the Sensitivity Indices. 4.7 FAST and Random Balance Designs. 4.8 Putting the Method to Work: the Infection Dynamics Model. 4.9 Caveats. 4.10 Exercises. 5. Factor Mapping and Metamodelling. 5.1 Introduction. 5.2 Monte Carlo Filtering (MCF). 5.2.1 Implementation of Monte Carlo Filtering. 5.2.2 Pros and Cons. 5.2.3 Exercises. 5.2.4 Solutions. 5.2.5 Examples. 5.3 Metamodelling and the High-Dimensional Model Representation. 5.3.1 Estimating HDMRs and Metamodels. 5.3.2 A Simple Example. 5.3.3 Another Simple Example. 5.3.4 Exercises. 5.3.5 Solutions to Exercises. 5.4 Conclusions. 6. Sensitivity Analysis: from Theory to Practice. 6.1 Example 1: a Composite Indicator. 6.1.1 Setting the Problem. 6.1.2 A Composite Indicator Measuring Countries’ Performance in Environmental Sustainability. 6.1.3 Selecting the Sensitivity Analysis Method. 6.1.4 The Sensitivity Analysis Experiment and its Results. 6.1.5 Conclusions. 6.2 Example 2: Importance of Jumps in Pricing Options. 6.2.1 Setting the Problem. 6.2.2 The Heston Stochastic Volatility Model with Jumps. 6.2.3 Selecting a Suitable Sensitivity Analysis Method. 6.2.4 The Sensitivity Analysis Experiment. 6.2.5 Conclusions. 6.3 Example 3: a Chemical Reactor. 6.3.1 Setting the Problem. 6.3.2 Thermal Runaway Analysis of a Batch Reactor. 6.3.3 Selecting the Sensitivity Analysis Method. 6.3.4 The Sensitivity Analysis Experiment and its Results. 6.3.5 Conclusions. 6.4 Example 4: a Mixed Uncertainty-Sensitivity Plot. 6.4.1 In Brief. 6.5 When to use What? Afterword. Bibliography. Index.

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Book SynopsisSplit into 4 accessible parts, the book presents: 1. An introduction to and definition of BBNs.2. Step-by-step practical guidelines to applying BBNs.3. A wide variety of applications in industry, natural sciences, services and computing.4. A discussion of the future directions BBN research and applications might take.Table of ContentsForeword ix Preface xi 1 Introduction to Bayesian networks 1 1.1 Models 1 1.2 Probabilistic vs. deterministic models 5 1.3 Unconditional and conditional independence 9 1.4 Bayesian networks 11 2 Medical diagnosis 15 2.1 Bayesian networks in medicine 15 2.2 Context and history 17 2.3 Model construction 19 2.4 Inference 26 2.5 Model validation 28 2.6 Model use 30 2.7 Comparison to other approaches 31 2.8 Conclusions and perspectives 32 3 Clinical decision support 33 3.1 Introduction 33 3.2 Models and methodology 34 3.3 The Busselton network 35 3.4 The PROCAM network 40 3.5 The PROCAM Busselton network 44 3.6 Evaluation 46 3.7 The clinical support tool: TakeHeartII 47 3.8 Conclusion 51 4 Complex genetic models 53 4.1 Introduction 53 4.2 Historical perspectives 54 4.3 Complex traits 56 4.4 Bayesian networks to dissect complex traits 59 4.5 Applications 64 4.6 Future challenges 71 5 Crime risk factors analysis 73 5.1 Introduction 73 5.2 Analysis of the factors affecting crime risk 74 5.3 Expert probabilities elicitation 75 5.4 Data preprocessing 76 5.5 A Bayesian network model 78 5.6 Results 80 5.7 Accuracy assessment 83 5.8 Conclusions 84 6 Spatial dynamics in France 87 6.1 Introduction 87 6.2 An indicator-based analysis 89 6.3 The Bayesian network model 97 6.4 Conclusions 109 7 Inference problems in forensic science 113 7.1 Introduction 113 7.2 Building Bayesian networks for inference 116 7.3 Applications of Bayesian networks in forensic science 120 7.4 Conclusions 126 8 Conservation of marbled murrelets in British Columbia 127 8.1 Context/history 127 8.2 Model construction 129 8.3 Model calibration, validation and use 136 8.4 Conclusions/perspectives 147 9 Classifiers for modeling of mineral potential 149 9.1 Mineral potential mapping 149 9.2 Classifiers for mineral potential mapping 151 9.3 Bayesian network mapping of base metal deposit 157 9.4 Discussion 166 9.5 Conclusions 171 10 Student modeling 173 10.1 Introduction 173 10.2 Probabilistic relational models 175 10.3 Probabilistic relational student model 176 10.4 Case study 180 10.5 Experimental evaluation 182 10.6 Conclusions and future directions 185 11 Sensor validation 187 11.1 Introduction 187 11.2 The problem of sensor validation 188 11.3 Sensor validation algorithm 191 11.4 Gas turbines 197 11.5 Models learned and experimentation 198 11.6 Discussion and conclusion 202 12 An information retrieval system 203 12.1 Introduction 203 12.2 Overview 205 12.3 Bayesian networks and information retrieval 206 12.4 Theoretical foundations 207 12.5 Building the information retrieval system 215 12.6 Conclusion 223 13 Reliability analysis of systems 225 13.1 Introduction 225 13.2 Dynamic fault trees 227 13.3 Dynamic Bayesian networks 228 13.4 A case study: The Hypothetical Sprinkler System 230 13.5 Conclusions 237 14 Terrorism risk management 239 14.1 Introduction 240 14.2 The Risk Influence Network 250 14.3 Software implementation 254 14.4 Site Profiler deployment 259 14.5 Conclusion 261 15 Credit-rating of companies 263 15.1 Introduction 263 15.2 Naive Bayesian classifiers 264 15.3 Example of actual credit-ratings systems 264 15.4 Credit-rating data of Japanese companies 266 15.5 Numerical experiments 267 15.6 Performance comparison of classifiers 273 15.7 Conclusion 276 16 Classification of Chilean wines 279 16.1 Introduction 279 16.2 Experimental setup 281 16.3 Feature extraction methods 285 16.4 Classification results 288 16.5 Conclusions 298 17 Pavement and bridge management 301 17.1 Introduction 301 17.2 Pavement management decisions 302 17.3 Bridge management 307 17.4 Bridge approach embankment – case study 308 17.5 Conclusion 312 18 Complex industrial process operation 313 18.1 Introduction 313 18.2 A methodology for Root Cause Analysis 314 18.3 Pulp and paper application 321 18.4 The ABB Industrial IT platform 325 18.5 Conclusion 326 19 Probability of default for large corporates 329 19.1 Introduction 329 19.2 Model construction 332 19.3 BayesCredit 335 19.4 Model benchmarking 341 19.5 Benefits from technology and software 342 19.6 Conclusion 343 20 Risk management in robotics 345 20.1 Introduction 345 20.2 DeepC 346 20.3 The ADVOCATE II architecture 352 20.4 Model development 354 20.5 Model usage and examples 360 20.6 Benefits from using probabilistic graphical models 361 20.7 Conclusion 362 21 Enhancing Human Cognition 365 21.1 Introduction 365 21.2 Human foreknowledge in everyday settings 366 21.3 Machine foreknowledge 369 21.4 Current application and future research needs 373 21.5 Conclusion 375 22 Conclusion 377 22.1 An artificial intelligence perspective 377 22.2 A rational approach of knowledge 379 22.3 Future challenges 384 Bibliography 385 Index 427

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Book SynopsisIn the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are beneficial, are superior to all alternatives and are cost-effective. Usually one study will not provide answers to these questions and it will be necessary to synthesize evidence from multiple sources.Table of ContentsPreface xi 1 Introduction 1 1.1 The rise of health economics 1 1.2 Decision making under uncertainty 4 1.2.1 Deterministic models 4 1.2.2 Probabilistic decision modelling 6 1.3 Evidence-based medicine 9 1.4 Bayesian statistics 10 1.5 NICE 11 1.6 Structure of the book 12 1.7 Summary key points 13 1.8 Further reading 13 References 14 2 Bayesian methods and WinBUGS 17 2.1 Introduction to Bayesian methods 17 2.1.1 What is a Bayesian approach? 17 2.1.2 Likelihood 18 2.1.3 Bayes’ theorem and Bayesian updating 19 2.1.4 Prior distributions 22 2.1.5 Summarising the posterior distribution 23 2.1.6 Prediction 24 2.1.7 More realistic and complex models 24 2.1.8 MCMC and Gibbs sampling 25 2.2 Introduction to WinBUGS 26 2.2.1 The BUGS language 26 2.2.2 Graphical representation 31 2.2.3 Running WinBUGS 32 2.2.4 Assessing convergence in WinBUGS 33 2.2.5 Statistical inference in WinBUGS 36 2.2.6 Practical aspects of using WinBUGS 39 2.3 Advantages and disadvantages of a Bayesian approach 39 2.4 Summary key points 40 2.5 Further reading 41 2.6 Exercises 41 References 42 3 Introduction to decision models 43 3.1 Introduction 43 3.2 Decision tree models 44 3.3 Model parameters 45 3.3.1 Effects of interventions 45 3.3.2 Quantities relating to the clinical epidemiology of the clinical condition being treated 50 3.3.3 Utilities 52 3.3.4 Resource use and costs 52 3.4 Deterministic decision tree 52 3.5 Stochastic decision tree 56 3.5.1 Presenting the results of stochastic economic decision models 60 3.6 Sources of evidence 66 3.7 Principles of synthesis for decision models (motivation for the rest of the book) 70 3.8 Summary key points 70 3.9 Further reading 71 3.10 Exercises 71 References 72 4 Meta-analysis using Bayesian methods 76 4.1 Introduction 76 4.2 Fixed Effect model 78 4.3 Random Effects model 81 4.3.1 The predictive distribution 83 4.3.2 Prior specification for τ 84 4.3.3 ‘Exact’ Random Effects model for Odds Ratios based on a Binomial likelihood 84 4.3.4 Shrunken study level estimates 86 4.4 Publication bias 87 4.5 Study validity 88 4.6 Summary key points 88 4.7 Further reading 88 4.8 Exercises 89 References 92 5 Exploring between study heterogeneity 94 5.1 Introduction 94 5.2 Random effects meta-regression models 95 5.2.1 Generic random effect meta-regression model 95 5.2.2 Random effects meta-regression model for Odds Ratio (OR) outcomes using a Binomial likelihood 98 5.2.3 Autocorrelation and centring covariates 100 5.3 Limitations of meta-regression 104 5.4 Baseline risk 105 5.4.1 Model for including baseline risk in a meta-regression on the (log) OR scale 107 5.4.2 Final comments on including baseline risk as a covariate 109 5.5 Summary key points 110 5.6 Further reading 110 5.7 Exercises 110 References 113 6 Model critique and evidence consistency in random effects meta-analysis 115 6.1 Introduction 115 6.2 The Random Effects model revisited 117 6.3 Assessing model fit 121 6.3.1 Deviance 121 6.3.2 Residual deviance 122 6.4 Model comparison 124 6.4.1 Effective number of parameters, pD 125 6.4.2 Deviance Information Criteria 126 6.5 Exploring inconsistency 127 6.5.1 Cross-validation 128 6.5.2 Mixed predictive checks 131 6.6 Summary key points 134 6.7 Further reading 134 6.8 Exercises 134 References 137 7 Evidence synthesis in a decision modelling framework 138 7.1 Introduction 138 7.2 Evaluation of decision models: One-stage vs two-stage approach 139 7.3 Sensitivity analyses (of model inputs and model specifications) 147 7.4 Summary key points 147 7.5 Further reading 147 7.6 Exercises 147 References 149 8 Multi-parameter evidence synthesis 151 8.1 Introduction 151 8.2 Prior and posterior simulation in a probabilistic model: Maple Syrup Urine Disease (MSUD) 152 8.3 A model for prenatal HIV testing 155 8.4 Model criticism in multi-parameter models 161 8.5 Evidence-based policy 163 8.6 Summary key points 164 8.7 Further reading 165 8.8 Exercises 166 References 167 9 Mixed and indirect treatment comparisons 169 9.1 Why go beyond ‘direct’ head-to-head trials? 169 9.2 A fixed treatment effects model for MTC 172 9.2.1 Absolute treatment effects 176 9.2.2 Relative treatment efficacy and ranking 176 9.3 Random Effects MTC models 178 9.4 Model choice and consistency of MTC evidence 179 9.4.1 Techniques for presenting and understanding the results of MTC 180 9.5 Multi-arm trials 181 9.6 Assumptions made in mixed treatment comparisons 182 9.7 Embedding an MTC within a cost-effectiveness analysis 183 9.8 Extension to continuous, rate and other outcomes 185 9.9 Summary key points 187 9.10 Further reading 187 9.11 Exercises 189 References 190 10 Markov models 193 10.1 Introduction 193 10.2 Continuous and discrete time Markov models 195 10.3 Decision analysis with Markov models 196 10.3.1 Evaluating Markov models 197 10.4 Estimating transition parameters from a single study 199 10.4.1 Likelihood 202 10.4.2 Priors and posteriors for multinomial probabilities 202 10.5 Propagating uncertainty in Markov parameters into a decision model 206 10.6 Estimating transition parameters from a synthesis of several studies 209 10.6.1 Challenges for meta-analysis of evidence on Markov transition parameters 209 10.6.2 The relationship between probabilities and rates 211 10.6.3 Modelling study effects 213 10.6.4 Synthesis of studies reporting aggregate data 215 10.6.5 Incorporating studies that provide event history data 217 10.6.6 Reporting results from a Random Effects model 219 10.6.7 Incorporating treatment effects 220 10.7 Summary key points 224 10.8 Further reading 224 10.9 Exercises 224 References 225 11 Generalised evidence synthesis 227 11.1 Introduction 227 11.2 Deriving a prior distribution from observational evidence 230 11.3 Bias allowance model for the observational data 233 11.4 Hierarchical models for evidence from different study designs 238 11.5 Discussion 244 11.6 Summary key points 244 11.7 Further reading 245 11.8 Exercises 246 References 248 12 Expected value of information for research prioritisation and study design 251 12.1 Introduction 251 12.2 Expected value of perfect information 256 12.3 Expected value of partial perfect information 259 12.3.1 Computation 261 12.3.2 Notes on EVPPI 264 12.4 Expected value of sample information 264 12.4.1 Computation 265 12.5 Expected net benefit of sampling 266 12.6 Summary key points 267 12.7 Further reading 268 12.8 Exercises 268 References 268 Appendix 1 Abbreviations 270 Appendix 2 Common distributions 272 A2.1 The Normal distribution 272 A2.2 The Binomial distribution 273 A2.3 The Multinomial distribution 273 A2.4 The Uniform distribution 274 A2.5 The Exponential distribution 274 A2.6 The Gamma distribution 275 A2.7 The Beta distribution 276 A2.8 The Dirichlet distribution 277 Index 278

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Book SynopsisAutoregressive Conditional Heteroskedastic (ARCH) processes are used in finance to model asset price volatility over time. This book introduces both the theory and applications of ARCH models and provides the basic theoretical and empirical background, before proceeding to more advanced issues and applications. The Authors provide coverage of the recent developments in ARCH modelling which can be implemented using econometric software, model construction, fitting and forecasting and model evaluation and selection. Key Features: Presents a comprehensive overview of both the theory and the practical applications of ARCH, an increasingly popular financial modelling technique. Assumes no prior knowledge of ARCH models; the basics such as model construction are introduced, before proceeding to more complex applications such as value-at-risk, option pricing and model evaluation. Uses empirical examples to demonstrate how the recent developments inTrade Review"Numerous articles on the Autoregressive Conditional Heteroskedastic (ARCH) process, an increasingly popular financial modeling technique, exist in various international journals. Now Xekalaki and Degiannakis (both statistics, Athens U. of Economics and Business, Greece) provide a thorough treatment of the ARCH theory and its practical applications, in a textbook for postgraduate and final-year undergraduate students which could serve as reference work for academics and financial market professionals." (Book News Inc, November 2010) Table of ContentsPrologue. Notation. 1 What is an ARCH process? 1.1 Introduction. 1.2 The Autoregressive Conditionally Heteroskedastic Process. 1.3 The Leverage Effect. 1.4 The Non-trading Period Effect. 1.5 Non-synchronous Trading Effect. 1.6 The Relationship between Conditional Variance and Conditional Mean. 2 ARCH Volatility Specifications. 2.1 Model Specifications. 2.2 Methods of Estimation. 2.3. Estimating the GARCH Model with EViews 6: An Empirical Example.. 2.4. Asymmetric Conditional Volatility Specifications. 2.5. Simulating ARCH Models Using EViews. 2.6. Estimating Asymmetric ARCH Models with G@RCH 4.2 OxMetrics – An Empirical Example.. 2.7. Misspecification Tests. 2.8 Other ARCH Volatility Specifications. 2.9 Other Methods of Volatility Modeling. 2.10 Interpretation of the ARCH Process. 3 Fractionally Integrated ARCH Models. 3.1 Fractionally Integrated ARCH Model Specifications. 3.2 Estimating Fractionally Integrated ARCH Models Using G@RCH 4.2 OxMetrics – An Empirical Example. 3.3 A More Detailed Investigation of the Normality of the Standardized Residuals – Goodness-of-fit Tests. 4 Volatility Forecasting: An Empirical Example Using EViews 6. 4.1 One-step-ahead Volatility Forecasting. 4.2 Ten-step-ahead Volatility Forecasting. 5 Other Distributional Assumptions. 5.1 Non-Normally Distributed Standardized Innovations. 5.2 Estimating ARCH Models with Non-Normally Distributed Standardized Innovations Using G@RCH 4.2 OxMetrics – An Empirical Example. 5.3 Estimating ARCH Models with Non-Normally Distributed Standardized Innovations Using EViews 6 – An Empirical Example. 5.4 Estimating ARCH Models with Non-Normally Distributed Standardized Innovations Using EViews 6 – The LogL Object. 6 Volatility Forecasting: An Empirical Example Using G@RCH Ox. 7 Intra-Day Realized Volatility Models. 7.1 Realized Volatility. 7.2 Intra-Day Volatility Models. 7.3 Intra-Day Realized Volatility & ARFIMAX Models in G@RCH 4.2 OxMetrics – An Empirical example. 8 Applications in Value-at-Risk, Expected Shortfalls, Options Pricing. 8.1 One-day-ahead Value-at-Risk Forecasting. 8.2 One-day-ahead Expected Shortfalls Forecasting. 8.3 FTSE100 Index: One-step-ahead Value-at-Risk and Expected Shortfall Forecasting. 8.4 Multi-period Value-at-Risk and Expected Shortfalls Forecasting. 8.5 ARCH Volatility Forecasts in Black and Scholes Option Pricing. 8.6 ARCH Option Pricing Formulas. 9 Implied Volatility Indices and ARCH Models. 9.1 Implied Volatility. 9.2 The VIX Index. 9.3 The Implied Volatility Index as an Explanatory Variable. 9.4 ARFIMAX Modeling for Implied Volatility Index. 10 ARCH Model Evaluation and Selection. 10.1 Evaluation of ARCH Models. 10.2 Selection of ARCH Models. 10.3 Application of Loss Functions as Methods of Model Selection.. 10.4 The SPA Test for VaR and Expected Shortfalls. 11 Multivariate ARCH Models. 11.1 Model Specifications. 11.2 Maximum Likelihood Estimation. 11.3 Estimating Multivariate ARCH Models Using EViews 6. 11.4 Estimating Multivariate ARCH Models Using G@RCH 5.0. 11.5 Evaluation of Multivariate ARCH Models. References. Author Index. Subject Index.

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Book SynopsisStatistical methodology plays a key role in ensuring that DNA evidence is collected, interpreted, analyzed and presented correctly. With the recent advances in computer technology, this methodology is more complex than ever before. There are a growing number of books in the area but none are devoted to the computational analysis of evidence. This book presents the methodology of statistical DNA forensics with an emphasis on the use of computational techniques to analyze and interpret forensic evidence.Table of ContentsPreface. List of figures. List of tables. 1. Introduction. 1.1 Statistics, forensic science and the law. 1.2 The use of statistics in forensic DNA. 1.3 Genetic basis of DNA profiling and typing technology. 1.3.1 Genetic basis. 1.3.2 Typing technology. 1.4 About the book. 2. Probability and statistics. 2.1 Probability. 2.2 Dependent events and conditional probability. 2.3 Law of total probability. 2.4 Bayes’ Theorem. 2.5 Binomial probability distribution. 2.6 Multinomial distribution. 2.7 Poisson distribution. 2.8 Normal distribution. 2.9 Likelihood ratio. 2.10 Statistical inference. 2.10.1 Test of hypothesis. 2.10.2 Estimation and testing. 2.11 Problems. 3. Population genetics. 3.1 Hardy-Weinberg equilibrium. 3.2 Test for Hardy-Weinberg equilibrium. 3.2.1 Observed and expected heterozygosities. 3.2.2 Chi-square test. 3.2.3 Fisher’s exact test. 3.2.4 Computer software. 3.3 Other statistics for analysis of a population database. 3.3.1 Linkage equilibrium. 3.3.2 Power of discrimination. 3.4 DNA profiling. 3.5 Subpopulation models. 3.6 Relatives. 3.7 Problems. 4. Parentage testing. 4.1 Standard trio. 4.1.1 Paternity index. 4.1.2 An example. 4.1.3 Posterior odds and probability of paternity. 4.2 Paternity computer software. 4.2.1 Steps in running the software. 4.2.2 The software to deal with an incest case. 4.3 A relative of the alleged father is the true father. 4.4 Alleged father unavailable but his relative is. 4.5 Motherless case. 4.5.1 Paternity index. 4.5.2 Computer software and example. 4.6 Motherless case: relatives involved. 4.6.1 A relative of the alleged father is the true father. 4.6.2 Alleged father unavailable but his relative is. 4.6.3 Computer software and example. 4.7 Determination of both parents. 4.8 Probability of excluding a random man from paternity. 4.9 Power of exclusion. 4.9.1 A random man case. 4.9.2 A relative case. 4.9.3 An elder brother case: mother available. 4.10 Other issues. 4.10.1 Reverse parentage. 4.10.2 Mutation. 4.11 Problems. 5. Testing for kinship. 5.1 Kinship testing of any two persons: HWE. 5.2 Computer software. 5.3 Kinship testing of two persons: subdivided populations. 5.3.1 Joint genotype probability. 5.3.2 Relatives involved. 5.4 Examples with software. 5.5 Three persons situation: HWE. 5.6 Computer software and example. 5.7 Three persons situation: subdivided populations. 5.7.1 Standard trio. 5.7.2 A relative of the alleged father is the true father. 5.7.3 Alleged father unavailable but his relative is. 5.7.4 Example. 5.7.5 General method and computer software. 5.8 Complex kinship determinations: method and software. 5.8.1 EasyPA_In_1_Minute software and the method. 5.8.2 EasyPAnt_In_1_Minute. 5.8.3 EasyIN_In_1_Minute. 5.8.4 EasyMISS_In_1_Minute. 5.8.5 Other considerations: probability of paternity and mutation. 5.9 Problems. 6. Interpreting mixtures. 6.1 An illustrative example. 6.2 Some common cases and a case example. 6.2.1 One victim, one suspect and one unknown. 6.2.2 One suspect and two unknowns. 6.2.3 Two suspects and two unknowns. 6.2.4 Case example. 6.2.5 Exclusion probability. 6.3 A general approach. 6.4 Population in Hardy-Weinberg equilibrium. 6.5 Population with multiple ethnic groups. 6.6 Subdivided population. 6.6.1 Single ethnic group: simple cases. 6.6.2 Single ethnic group: general situations. 6.6.3 Multiple ethnic groups. 6.7 Computer software and example. 6.8 NRC II Recommendation 4.1. 6.8.1 Single ethnic group. 6.8.2 Multiple ethnic groups. 6.9 Proofs. 6.9.1 The proof of Equation (6.6). 6.9.2 The proof of Equation (6.8). 6.9.3 The proof of Equation (6.9). 6.9.4 The proofs of Equations (6.11) and (6.12). 6.9.5 The proofs of Equations (6.14) and (6.15). 6.10 Problems. 7. Interpreting mixtures in the presence of relatives. 7.1 One pair of relatives: HWE. 7.1.1 Motivating example. 7.1.2 A probability formula. 7.1.3 Tested suspect with an unknown relative. 7.1.4 Unknown suspect with a tested relative. 7.1.5 Two related persons were unknown contributors. 7.1.6 An application. 7.2 Two pairs of relatives: HWE. 7.2.1 Two unknowns related respectively to two typed persons. 7.2.2 One unknown is related to a typed person and two other. unknowns are related. 7.2.3 Two pairs of related unknowns. 7.2.4 Examples. 7.2.5 Extension. 7.3 Related people from the same subdivided population. 7.3.1 Introductory example. 7.3.2 A simple case with one victim, one suspect and one relative. 7.3.3 General formulas. 7.3.4 An example analyzed by the software. 7.4 Proofs. 7.4.1 Preliminary. 7.4.2 The proof of Equation (7.5). 7.4.3 The proof of Equation (7.7). 7.4.4 The proof of Equation (7.9). 7.4.5 The proof of Equation (7.11). 7.4.6 The proof of Equation (7.13). 7.4.7 The proofs of Equations (7.18) and (7.20). 7.5 Problems. 8. Other issues. 8.1 Lineage markers. 8.2 Haplotypic genetic markers for mixture. 8.3 Bayesian network. 8.4 Peak information. 8.5 Mass disaster. 8.6 Database search. Solutions to Problems. Appendix A: The standard normal distribution. Appendix B: Upper 1% and 5% points of w2 distributions. Bibliography. Index.

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Book SynopsisAn up-to-date and comprehensive treatment of biosurveillance techniques With the worldwide awareness of bioterrorism and drug-resistant infectious diseases, the need for surveillance systems to accurately detect emerging epidemicsis essential for maintaining global safety.Trade Review“The book is especially valuable for anyone interested in automated disease surveillance because of its broad scope addressing all issues related to developing and operating automated disease surveillance systems.” (Biometrics, June 2009 ) "This book is essential reading for those learning about public health disease surveillance and for statisticians working with public health professional to improve the sensitivity, specificity, timeliness and cost-effectiveness of current surveillance systems." (Journal of the American Statistical Association, June 2008) "…creates a roadmap for scientists to follow…" (Electric Review, June/July 2007)Table of Contents1. Disease Surveillance (J. Lombardo & D. Ross). 2. Understanding the Data (S. Babin, et al.). 3. Obtaining the Data (R. Wojcik, et al.). 4. Alerting Algorithms for Biosurveillance (H. Burkom). 5. Putting It Together (L. Hauenstein, et al.). 6. Modern Disease Surveillance (S. Lewis, et al.). 7. Canadian Applications (J. Aramini & S. Mukhi). 8. Telehealth in England and Wales (D. Cooper). 9. EWORS amd Alerta DISAMAR (J. Chretien, et al.). 10. Evaulating Automated Surveillance Systems (D. Buckeridge, et al.). 11. Educating the Workforce (H. Lehmann). 12. The Road Ahead (J. Lombardo). Index.

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Book SynopsisThis revised and expanded edition presents the analytic modeling of queues using up-to-date examples. It contains additional material (call centers and simulation), discussions (Transform Approximation Method and Level Crossing Analysis), and exercises.Trade Review"Despite its title, the book is rather advanced, so it is appropriate for practitioners, those in academia, and upper-class students. However, any reader will benefit from the concise introductions to the problems, the detailed descriptions supported with step-by-step formulas, the solutions provided by the manual, and the QtsPlus software." (Computing Reviews, 1 December 2011) Table of ContentsDedication v Preface xi Acknowledgments xiii Introduction 1 Description of the Queueing Problem 2 Characteristics of Queueing Processes 3 Notation 7 Measuring System Performance 8 Some General Results 9 Simple Data Bookkeeping for Queues 12 Poisson Process and the Exponential Distribution 16 Markovian Property of the Exponential Distribution 20 Stochastic Processes and Markov Chains 24 Introduction to the QtsPlus Software 40 Problems 41 Simple Markovian Queueing Models 49 Birth-Death Processes 49 Single-Server Queues (M/M/1) 53 Multiserver Queues (M/M/c) 66 Choosing the Number of Servers 73 Queues with Truncation (M/M/c/K) 76 Erlang's Loss Formula (M/M/c/c) 81 Queues with Unlimited Service (M/M/[infinity]) 84 Finite-Source Queues 85 State-Dependent Service 91 Queues with Impatience 95 Transient Behavior 97 Busy-Period Analysis 102 Problems 103 Advanced Markovian Queueing Models 117 Bulk Input (M[superscript X]/M/1) 117 Bulk Service (M/M[superscript Y]/1) 123 Erlangian Models 128 Priority Queue Disciplines 141 Retrial Queues 157 Problems 171 Networks, Series, and Cyclic Queues 179 Series Queues 181 Open Jackson Networks 187 Closed Jackson Networks 195 Cyclic Queues 209 Extensions of Jackson Networks 210 Non-Jackson Networks 212 Problems 214 General Arrival or Service Patterns 219 General Service, Single Server (M/G/1) 219 General Service, Multiserver (M/G/c/[infinity], M/G/[infinity]) 254 General Input (G/M/1, G/M/c) 259 Problems 270 General Models and Theoretical Topics 277 G/E[subscript k]/1, G[superscript k]/M/1, and G/PH[subscript k]/1 277 General Input, General Service (G/G/1) 284 Poisson Input, Constant Service, Multiserver (M/D/c) 294 Semi-Markov and Markov Renewal Processes in Queueing 296 Other Queue Disciplines 301 Design and Control of Queues 306 Statistical Inference in Queueing 317 Problems 325 Bounds and Approximations 329 Bounds 330 Approximations 343 Network Approximations 356 Problems 367 Numerical Techniques and Simulation 369 Numerical Techniques 369 Numerical Inversion of Transforms 385 Discrete-Event Stochastic Simulation 398 Problems 421 References 427 Symbols and Abbreviations 439 Tables 447 Transforms and Generating Functions 455 Laplace Transforms 455 Generating Functions 462 Differential and Difference Equations 467 Ordinary Differential Equations 467 Difference Equations 483 QtsPlus Software 489 Instructions for Downloading 493 Index 495

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Book SynopsisOver the years, I have had the opportunity to teach several regression courses, and I cannot think of a better undergraduate text than this one.The American Statistician The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite. I also highly recommend it to practitioners who want to solve real-life prediction problems. (Computing Reviews) Modern Regression Methods, Second Edition maintains the accessible organization, breadth of coverage, and cutting-edge appeal that earned its predecessor the title of being one of the top five books for statisticians by an Amstat News book editor in 2003. This new edition has been updated and enhanced to include all-new information on the latest advances and research in the evolving field of regression analysis. The book provides a unique treatment of fundamental regression methods, suTrade Review"The book is to be praised in that it makes the reader aware of a large number of approaches to regression situations, and also to their possible pitfalls. It is thus an excellent basis for an experienced instructor to teach regression at different levels." (Springer, August 2010) "This book, at the undergraduate level and even at the graduate level, will be rewarding reading for anyone interested in learning the nuances of regression analysis." (Mathmatical Reviews, January 2010) "The exercises are interesting and thought-provoking throughout. If you liked the first edition, you will be pleased with this revision also." (International Statistical Review, August 2009) "The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite. I also highly recommend it to practitioners who want to solve real-life prediction problems." (Computing Reviews, July 2009) "In this second edition, Ryan (author, editor, and educator) provides substantial updates and revisions of his popular text for statisticians to include new information on the most current advances and research in regression analysis" (SciTech Reviews, March 2009) "One would be hard-pressed to find another text that rivals this one in terms of coverage of the regression literature." (The American Statistician, 2009) "I strongly recommend the book as a reference for anyone teaching or using regression." (MAA Reviews, 2009) "Highly recommended for those already trained in mathematics and statistics who want a good guide to current practice and issues in multiple regression techniques." (Journal of Biopharmaceutical Statistics, 2009)Table of ContentsPreface. 1. Introduction. 1.1 Simple Linear Regression Model. 1.2 Uses of Regression Models. 1.3 Graph the Data! 1.4 Estimation of ß0 and ß1. 1.5 Inferences from Regression Equations. 1.6 Regression Through the Origin. 1.7 Additional Examples. 1.8 Correlation. 1.9 Miscellaneous Uses of Regression. 1.10 Fixed Versus Random Regressors. 1.11 Missing Data. 1.12 Spurious Relationships. 1.13 Software. 1.14 Summary. Appendix. References. Exercises. 2. Diagnostics and Remedial Measures. 2.1 Assumptions. 2.2 Residual Plots. 2.3 Transformations. 2.4 Influential Observations. 2.5 Outliers. 2.6 Measurement Error. 2.7 Software. 2.8 Summary. Appendix. References. Exercises. 3. Regression with Matrix Algebra. 3.1 Introduction to Matrix Algebra. 3.2 Matrix Algebra Applied to Regression. 3.3 Summary. Appendix. References. Exercises. 4. Introduction to Multiple Linear Regression. 4.1 An Example of Multiple Linear Regression. 4.2 Centering And Scaling. 4.3 Interpreting Multiple Regression Coefficients. 4.4 Indicator Variables. 4.5 Separation or Not? 4.6 Alternatives to Multiple Regression. 4.7 Software. 4.8 Summary. References. Exercises. 5. Plots in Multiple Regression. 5.1 Beyond Standardized Residual Plots. 5.2 Some Examples. 5.3 Which Plot? 5.4 Recommendations. 5.5 Partial Regression Plots. 5.6 Other Plots For Detecting Influential Observations. 5.7 Recent Contributions to Plots in Multiple Regression. 5.8 Lurking Variables. 5.9 Explanation of Two Data Sets Relative to R2. 5.10 Software. 5.11 Summary. References. Exercises. 6. Transformations in Multiple Regression. 6.1 Transforming Regressors. 6.2 Transforming Y. 6.3 Further Comments on the Normality Issue. 6.4 Box-Cox Transformation. 6.5 Box-Tidwell Revisited. 6.6 Combined Box-Cox and Box-Tidwell Approach. 6.7 Other Transformation Methods. 6.8 Transformation Diagnostics. 6.9 Software. 6.10 Summary. References. Exercises. 7. Selection of Regressors. 7.1 Forward Selection. 7.2 Backward Elimination. 7.3 Stepwise Regression. 7.4 All Possible Regressions. 7.5 Newer Methods. 7.6 Examples. 7.7 Variable Selection for Nonlinear Terms. 7.8 Must We Use a Subset? 7.9 Model Validation. 7.10 Software. 7.11 Summary. Appendix. References. Exercises. 8. Polynomial and Trigonometric Terms. 8.1 Polynomial Terms. 8.2 Polynomial-Trigonometric Regression. 8.3 Software. 8.4 Summary. References. Exercises. 9. Logistic Regression. 9.1 Introduction. 9.2 One Regressor. 9.3 A Simulated Example. 9.4 Detecting Complete Separation, Quasicomplete Separation and Near Separation. 9.5 Measuring the Worth of the Model. 9.6 Determining the Worth of the Individual Regressors. 9.7 Confidence Intervals. 9.8 Exact Prediction. 9.9 An Example With Real Data. 9.10 An Example of Multiple Logistic Regression. 9.11 Multicollinearity in Multiple Logistic Regression. 9.12 Osteogenic Sarcoma Data Set. 9.13 Missing Data. 9.14 Sample Size Determination. 9.15 Polytomous Logistic Regression. 9.16 Logistic Regression Variations. 9.17 Alternatives to Logistic Regression. 9.18 Software for Logistic Regression. 9.19 Summary. Appendix. References. Exercises. 10. Nonparametric Regression. 10.1 Relaxing Regression Assumptions. 10.2 Monotone Regression. 10.3 Smoothers. 10.4 Variable Selection. 10.5 Important Considerations in Smoothing. 10.6 Sliced Inverse Regression. 10.7 Projection Pursuit Regression. 10.8 Software. 10.9 Summary. Appendix. References. Exercises. 11. Robust Regression. 11.1 The Need for Robust Regression. 11.2 Types of Outliers. 11.3 Historical Development of Robust Regression. 11.4 Goals of Robust Regression. 11.5 Proposed High Breakdown Point Estimators. 11.6 Approximating HBP Estimator Solutions. 11.7 Other Methods for Detecting Multiple Outliers. 11.8 Bounded Influence Estimators. 11.9 Multistage Procedures. 11.10 Other Robust Regression Estimators. 11.11 Applications. 11.12 Software for Robust Regression. 11.13 Summary. References. Exercises. 12. Ridge Regression. 12.1 Introduction. 12.2 How Do We Determine K? 12.3 An Example. 12.4 Ridge Regression for Prediction. 12.5 Generalized Ridge Regression. 12.6 Inferences in Ridge Regression. 12.7 Some Practical Considerations. 12.8 Robust Ridge Regression? 12.9 Recent Developments in Ridge Regression. 12.10 Other Biased Estimators. 12.11 Software. 12.12 Summary. Appendix. References. Exercises. 13. Nonlinear Regression. 13.1 Introduction. 13.2 Linear Versus Nonlinear Regression. 13.3 A Simple Nonlinear Example. 13.4 Relative Offset Convergence Criterion. 13.5 Adequacy of the Estimation Approach. 13.6 Computational Considerations. 13.7 Determining Model Adequacy. 13.7.1 Lack-of-Fit Test. 13.8 Inferences. 13.9 An Application. 13.10 Rational Functions. 13.11 Robust Nonlinear Regression. 13.12 Applications. 13.13 Teaching Tools. 13.14 Recent Developments. 13.15 Software. 13.16 Summary. Appendix. References. Exercises. 14. Experimental Designs for Regression. 14.1 Objectives for Experimental Designs. 14.2 Equal Leverage Points. 14.3 Other Desirable Properties of Experimental Designs. 14.4 Model Misspecification. 14.5 Range of Regressors. 14.6 Algorithms for Design Construction. 14.7 Designs for Polynomial Regression. 14.8 Designs for Logistic Regression. 14.9 Designs for Nonlinear Regression. 14.10 Software. 14.11 Summary. References. Exercises. 15. Miscellaneous Topics in Regression. 15.1 Piecewise Regression and Alternatives. 15.2 Semiparametric Regression. 15.3 Quantile Regression. 15.4 Poisson Regression. 15.5 Negative Binomial Regression. 15.6 Cox Regression. 15.7 Probit Regression. 15.8 Censored Regression and Truncated Regression. 15.8.1 Tobit Regression. 15.9 Constrained Regression. 15.10 Interval Regression. 15.11 Random Coefficient Regression. 15.12 Partial Least Squares Regression. 15.13 Errors-in-Variables Regression. 15.14 Regression with Life Data. 15.15 Use of Regression in Survey Sampling. 15.16 Bayesian Regression. 15.17 Instrumental Variables Regression. 15.18 Shrinkage Estimators. 15.19 Meta-Regression. 15.20 Classification and Regression Trees (CART). 15.21 Multivariate Regression. References. Exercises. 16. Analysis of Real Data Sets. 16.1 Analyzing Buchanan’s Presidential Vote in Palm Beach County in 2000. 16.2 Water Quality Data. 16.3 Predicting Lifespan? 16.4 Scottish Hill Races Data. 16.5 Leukemia Data. 16.6 Dosage Response Data. 16.7 A Strategy for Analyzing Regression Data. 16.8 Summary. References. Answers to Selected Exercises. Statistical Tables. Author Index. Subject Index.

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Book SynopsisThe objective of this book is to motivate an appreciation of contemporary statistical techniques within the context of engineering. The author presents an optimum blend between statistical thinking and statistical methodology through emphasis of a broad sweep of tools rather than endless streams of seemingly unrelated methods and formulae.Trade Review"Overall this is an excellent book, which defines a broader mandate than many of its competing texts. By providing, clear, understandable discussion of the basics of statistics through to more advanced methods commonly used by engineers, this book is an essential reference for practitioners, and an ideal text for a two semester course introducing engineers to the power and utility of statistics." (The American Statistician, August 2008) "In this book on modern engineering statistics, Ryan does an excellent job of providing the appropriate statistical concepts and tools using engineering resources.... Highly recommended. Lower- and upper-division undergraduates" (CHOICE, April 2008) "This self-contained volume motivates an appreciation of statistical techniques within the context of engineering; many datasets that are used in the chapters and exercises are from engineering sources. This book is ideal for either a one- or two-semester course in engineering statistics." (Computing Reviews, April 2008)Table of ContentsPreface xvii 1. Methods of Collecting and Presenting Data 1 1.1 Observational Data and Data from Designed Experiments 3 1.2 Populations and Samples 5 1.3 Variables 6 1.4 Methods of Displaying Small Data Sets 7 1.5 Methods of Displaying Large Data Sets 16 1.6 Outliers 22 1.7 Other Methods 22 1.8 Extremely Large Data Sets: Data Mining 23 1.9 Graphical Methods: Recommendations 23 1.10 Summary 24 References 24 Exercises 25 2. Measures of Location and Dispersion 45 2.1 Estimating Location Parameters 46 2.2 Estimating Dispersion Parameters 50 2.3 Estimating Parameters from Grouped Data 55 2.4 Estimates from a Boxplot 57 2.5 Computing Sample Statistics with MINITAB 58 2.6 Summary 58 Reference 58 Exercises 58 3. Probability and Common Probability Distributions 68 3.1 Probability: From the Ethereal to the Concrete 68 3.3 Common Discrete Distributions 76 3.4 Common Continuous Distributions 92 3.5 General Distribution Fitting 106 3.6 How to Select a Distribution 107 3.7 Summary 108 References 109 Exercises 109 4. Point Estimation 121 4.1 Point Estimators and Point Estimates 121 4.2 Desirable Properties of Point Estimators 121 4.3 Distributions of Sampling Statistics 125 4.4 Methods of Obtaining Estimators 128 4.5 Estimating σθ 132 4.6 Estimating Parameters Without Data 133 4.7 Summary 133 References 134 Exercises 134 5. Confidence Intervals and Hypothesis Tests—One Sample 140 5.1 Confidence Interval for μ: Normal Distribution σ Not Estimated from Sample Data 140 5.2 Confidence Interval for μ: Normal Distribution σ Estimated from Sample Data 146 5.3 Hypothesis Tests for μ: Using Z and t 147 5.4 Confidence Intervals and Hypothesis Tests for a Proportion 157 5.5 Confidence Intervals and Hypothesis Tests for σ2 and σ 161 5.6 Confidence Intervals and Hypothesis Tests for the Poisson Mean 164 5.7 Confidence Intervals and Hypothesis Tests When Standard Error Expressions are Not Available 166 5.8 Type I and Type II Errors 168 5.9 Practical Significance and Narrow Intervals: The Role of n 172 5.10 Other Types of Confidence Intervals 173 5.11 Abstract of Main Procedures 174 5.12 Summary 175 Appendix: Derivation 176 References 176 Exercises 177 6. Confidence Intervals and Hypothesis Tests—Two Samples 189 6.1 Confidence Intervals and Hypothesis Tests for Means: Independent Samples 189 6.2 Confidence Intervals and Hypothesis Tests for Means: Dependent Samples 197 6.3 Confidence Intervals and Hypothesis Tests for Two Proportions 200 6.4 Confidence Intervals and Hypothesis Tests for Two Variances 202 6.5 Abstract of Procedures 204 6.6 Summary 205 References 205 Exercises 205 7. Tolerance Intervals and Prediction Intervals 214 7.1 Tolerance Intervals: Normality Assumed 215 7.2 Tolerance Intervals and Six Sigma 219 7.3 Distribution-Free Tolerance Intervals 219 7.4 Prediction Intervals 221 7.5 Choice Between Intervals 227 7.6 Summary 227 References 228 Exercises 229 8. Simple Linear Regression Correlation and Calibration 232 8.1 Introduction 232 8.2 Simple Linear Regression 232 8.3 Correlation 254 8.4 Miscellaneous Uses of Regression 256 8.5 Summary 264 References 264 Exercises 265 9. Multiple Regression 276 9.1 How Do We Start? 277 9.2 Interpreting Regression Coefficients 278 9.3 Example with Fixed Regressors 279 9.4 Example with Random Regressors 281 9.5 Example of Section 8.2.4 Extended 291 9.6 Selecting Regression Variables 293 9.7 Transformations 299 9.8 Indicator Variables 300 9.9 Regression Graphics 300 9.10 Logistic Regression and Nonlinear Regression Models 301 9.11 Regression with Matrix Algebra 302 9.12 Summary 302 References 303 Exercises 304 10. Mechanistic Models 314 10.1 Mechanistic Models 315 10.2 Empirical–Mechanistic Models 316 10.3 Additional Examples 324 10.4 Software 325 10.5 Summary 326 References 326 Exercises 327 11. Control Charts and Quality Improvement 330 11.1 Basic Control Chart Principles 330 11.2 Stages of Control Chart Usage 331 11.3 Assumptions and Methods of Determining Control Limits 334 11.4 Control Chart Properties 335 11.5 Types of Charts 336 11.6 Shewhart Charts for Controlling a Process Mean and Variability (Without Subgrouping) 336 11.7 Shewhart Charts for Controlling a Process Mean and Variability (With Subgrouping) 344 11.8 Important Use of Control Charts for Measurement Data 349 11.9 Shewhart Control Charts for Nonconformities and Nonconforming Units 349 11.10 Alternatives to Shewhart Charts 356 11.11 Finding Assignable Causes 359 11.12 Multivariate Charts 362 11.13 Case Study 362 11.14 Engineering Process Control 364 11.15 Process Capability 365 11.16 Improving Quality with Designed Experiments 366 11.17 Six Sigma 367 11.18 Acceptance Sampling 368 11.19 Measurement Error 368 11.20 Summary 368 References 369 Exercises 370 12. Design and Analysis of Experiments 382 12.1 Processes Must be in Statistical Control 383 12.2 One-Factor Experiments 384 12.3 One Treatment Factor and at Least One Blocking Factor 392 12.4 More Than One Factor 395 12.5 Factorial Designs 396 12.6 Crossed and Nested Designs 405 12.7 Fixed and Random Factors 406 12.8 ANOM for Factorial Designs 407 12.9 Fractional Factorials 409 12.10 Split-Plot Designs 413 12.11 Response Surface Designs 414 12.12 Raw Form Analysis Versus Coded Form Analysis 415 12.13 Supersaturated Designs 416 12.14 Hard-to-Change Factors 416 12.15 One-Factor-at-a-Time Designs 417 12.16 Multiple Responses 418 12.17 Taguchi Methods of Design 419 12.18 Multi-Vari Chart 420 12.19 Design of Experiments for Binary Data 420 12.20 Evolutionary Operation (EVOP) 421 12.21 Measurement Error 422 12.22 Analysis of Covariance 422 12.23 Summary of MINITAB and Design-Expert® Capabilities for Design of Experiments 422 12.24 Training for Experimental Design Use 423 12.25 Summary 423 Appendix A Computing Formulas 424 Appendix B Relationship Between Effect Estimates and Regression Coefficients 426 References 426 Exercises 428 13. Measurement System Appraisal 441 13.1 Terminology 442 13.2 Components of Measurement Variability 443 13.3 Graphical Methods 449 13.4 Bias and Calibration 449 13.5 Propagation of Error 454 13.6 Software 455 13.7 Summary 456 References 456 Exercises 457 14. Reliability Analysis and Life Testing 460 14.1 Basic Reliability Concepts 461 14.2 Nonrepairable and Repairable Populations 463 14.3 Accelerated Testing 463 14.4 Types of Reliability Data 466 14.5 Statistical Terms and Reliability Models 467 14.6 Reliability Engineering 473 14.7 Example 474 14.8 Improving Reliability with Designed Experiments 474 14.9 Confidence Intervals 477 14.10 Sample Size Determination 478 14.11 Reliability Growth and Demonstration Testing 479 14.12 Early Determination of Product Reliability 480 14.13 Software 480 14.14 Summary 481 References 481 Exercises 482 15. Analysis of Categorical Data 487 15.1 Contingency Tables 487 15.2 Design of Experiments: Categorical Response Variable 497 15.3 Goodness-of-Fit Tests 498 15.4 Summary 500 References 500 Exercises 501 16. Distribution-Free Procedures 507 16.1 Introduction 507 16.2 One-Sample Procedures 508 16.3 Two-Sample Procedures 512 16.4 Nonparametric Analysis of Variance 514 16.5 Exact Versus Approximate Tests 519 16.6 Nonparametric Regression 519 16.7 Nonparametric Prediction Intervals and Tolerance Intervals 521 16.8 Summary 521 References 521 Exercises 522 17. Tying It All Together 525 17.1 Review of Book 525 17.2 The Future 527 17.3 Engineering Applications of Statistical Methods 528 Reference 528 Exercises 528 Answers to Selected Excercises 533 Appendix: Statistical Tables 562 Table A Random Numbers 562 Table B Normal Distribution 564 Table C t-Distribution 566 Table D F-Distribution 567 Table E Factors for Calculating Two-Sided 99% Statistical Intervals for a Normal Population to Contain at Least 100p% of the Population 570 Table F Control Chart Constants 571 Author Index 573 Subject Index 579

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Book SynopsisFinally there's an easy-to-follow book that will help readers succeed in the art of proving theorems. Sibley not only conveys the spirit of mathematics but also uncovers the skills required to succeed. Key definitions are introduced while readers are encouraged to develop an intuition about these concepts and practice using them in problems.Table of ContentsPART I Chapter 1: LANGUAGE, LOGIC, AND SETS 1.1 Logic and Language 1.2 Implication 1.3 Quantifiers and Definitions 1.4 Introduction to Sets 1.5 Introduction to Number Theory 1.6 Additional Set Theory Definitions from Chapter 1 Algebraic and Order Properties of Number Systems Chapter 2: PROOFS 2.1 Proof Format I: Direct Proofs 2.2 Proof Format II: Contrapositive and Contradition 2.3 Proof Format III: Existence, Uniqueness, Or 2.4 Proof Format IV: Mathematical Induction The Fundamental Theorem of Arithmetic 2.5 Further Advice and Practice in Proving Proof Formats Chapter 3: FUNCTIONS 3.1 Definitions 3.2 Composition, One-to-One, Onto, and Inverses 3.3 Images and Pre-Images of Sets Definitions from Chapter 3 Chapter 4: RELATIONS 4.1 Relations 4.2 Equivalence Relations 4.3 Partitions and Equivalence Relations 4.4 Partial Orders Definitions from Chapter 4 PART II Chapter 5: INFINTE SETS 5.1 The Sizes of Sets 5.2 Countable Sets 5.3 Uncountable Sets 5.4 The Axiom of Choice and Its Equivalents Definitions from Chapter 5 Chapter 6: INTRODUCTION TO DISCRETE MATHEMATICS 6.1 Graph Theory 6.2 Trees and Algorithms 6.3 Counting Principles I 6.4 Counting Principles II Definitions from Chapter 6 Chapter 7: INTRODUCTION TO ABSTRACT ALGEBRA 7.1 Operations and Properties 7.2 Groups Groups in Geometry 7.3 Rings and Fields 7.4 Lattices 7.5 Homomorphisms Definitions from Chapter 7 Chapter 8: INTRODUCTION TO ANALYSIS 8.1 Real Numbers, Approximations, and Exact Values Zeno’s Paradoxes 8.2 Limits of Functions 8.3 Continuous Functions and Counterexamples Counterexamples in Rational Analysis 8.4 Sequences and Series 8.5 Discrete Dynamical Systems The Intermediate Value Theorem Definitions for Chapter 8 Chapter 9: METAMATHEMATICS AND THE PHILOSOPHY OF MATHEMATICS 9.1 Metamathematics 9.2 The Philosophy of Mathematics Definitions for Chapter 9 Appendix: THE GREEK ALPHABET Answers: SELECTED ANSWERS Index List of Symbols

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Book SynopsisThe first book to present a unified account of symbolic data analysis methods in a consistent statistical framework, Symbolic Data Analysis features a substantial number of examples from a range of application areas, including health, the social sciences, economics, and computer science.Trade Review“Primarily aimed at statisticians and Data analysts, SDA is also ideal for scientists…” (Zentralblatt MATH, 2007)Table of Contents1. Introduction. References. 2. Symbolic Data. 2.1 Symbolic and Classical Data. 2.2 Categories, Concepts and Symbolic Objects. 2.3 Comparison of Symbolic and Classical Analysis. 3. Basic Descriptive Statistics: One Variate. 3.1 Some Preliminaries. 3.2 Multi-valued Variables. 3.3 Interval-valued Variables. 3.4 Multi-valued Modal variables. 3.5 Interval-valued Modal Variables. 4. Descriptive Statistics: Two or More Variates. 4.1 Multi-valued Variables. 4.2 Interval-valued Variables. 4.3 Modal Multi-valued Variables. 4.4 Modal Interval-valued Variables. 4.5 Baseball Interval-valued Dataset. 4.6 Measures of Dependence. 5. Principal Component Analysis. 5.1 Vertices Method. 5.2 Centers Method. 5.3 Comparison of the Methods. 6. Regression Analysis. 6.1 Classical Multiple Regression Model. 6.2 Multi-valued Variables. 6.3 Interval-valued Variables. 6.4 Histogram-valued Variables. 6.5 Taxonomy Variables. 6.6 Hierarchical Variables. 7. Cluster Analysis. 7.1 Dissimilarity and Distance Measures. 7.2 Clustering Structures. 7.3 Partitions. 7.4 Hierarchy-Divisive Clustering. 7.5 Hierarchy-Pyramid Clusters. Data Index. Author Index. Subject Index.

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Book SynopsisThis bookbrings together acollection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics andreal-worldexampleswhich do not feature in many standard texts.The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area. Trade Review"I congratulate the editors on this volume; it really is an essential and very enjoyable journey with Don Rubin's statistical family." (Biometrics, September 2006) "…contains much current important work…" (Technometrics, November 2005) "This a useful reference book on an important topic with applications to a wide range of disciplines." (CHOICE, September 2005) “With this variety of papers, the reader is bound to find some papers interesting…” (Journal of Applied Statistics, Vol.32, No.3, April 2005) “I strongly recommend that libraries have a copy of this book in their reference section.” (Journal of the Royal Statistical Society Series A, June 2005) "...a very useful addition to academic libraries…" (Short Book Reviews, Vol.24, No.3, December 2004)Table of ContentsPreface. I Casual inference and observational studies. 1 An overview of methods for causal inference from observational studies, by Sander Greenland. 1.1 Introduction. 1.2 Approaches based on causal models. 1.3 Canonical inference. 1.4 Methodologic modeling. 1.5 Conclusion. 2 Matching in observational studies, by Paul R. Rosenbaum. 2.1 The role of matching in observational studies. 2.2 Why match? 2.3 Two key issues: balance and structure. 2.4 Additional issues. 3 Estimating causal effects in nonexperimental studies, by Rajeev Dehejia. 3.1 Introduction. 3.2 Identifying and estimating the average treatment effect. 3.3 The NSWdata. 3.4 Propensity score estimates. 3.5 Conclusions. 4 Medication cost sharing and drug spending in Medicare, by Alyce S. Adams. 4.1 Methods. 4.2 Results. 4.3 Study limitations. 4.4 Conclusions and policy implications. 5 A comparison of experimental and observational data analyses, by Jennifer L. Hill, Jerome P. Reiter, and Elaine L. Zanutto. 5.1 Experimental sample. 5.2 Constructed observational study. 5.3 Concluding remarks. 6 Fixing broken experiments using the propensity score, by Bruce Sacerdote. 6.1 Introduction. 6.2 The lottery data. 6.3 Estimating the propensity scores. 6.4 Results. 6.5 Concluding remarks. 7 The propensity score with continuous treatments, by Keisuke Hirano and Guido W. Imbens. 7.1 Introduction. 7.2 The basic framework. 7.3 Bias removal using the GPS. 7.4 Estimation and inference. 7.5 Application: the Imbens–Rubin–Sacerdote lottery sample. 7.6 Conclusion. 8 Causal inference with instrumental variables, by Junni L. Zhang. 8.1 Introduction. 8.2 Key assumptions for the LATE interpretation of the IV estimand. 8.3 Estimating causal effects with IV. 8.4 Some recent applications. 8.5 Discussion. 9 Principal stratification, by Constantine E. Frangakis. 9.1 Introduction: partially controlled studies. 9.2 Examples of partially controlled studies. 9.3 Principal stratification. 9.4 Estimands. 9.5 Assumptions. 9.6 Designs and polydesigns. II Missing data modeling. 10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues, by John L. Eltinge. 10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies. 10.2 Constraints. 10.3 Complex estimand structures, inferential goals, and utility functions. 10.4 Robustness. 10.5 Closing remarks. 11 Bridging across changes in classification systems, by Nathaniel Schenker. 11.1 Introduction. 11.2 Multiple imputation to achieve comparability of industry and occupation codes. 11.3 Bridging the transition from single-race reporting to multiple-race reporting. 11.4 Conclusion. 12 Representing the Census undercount by multiple imputation of households, by Alan M. Zaslavsky. 12.1 Introduction. 12.2 Models. 12.3 Inference. 12.4 Simulation evaluations. 12.5 Conclusion. 13 Statistical disclosure techniques based on multiple imputation, by Roderick J. A. Little, Fang Liu, and Trivellore E. Raghunathan. 13.1 Introduction. 13.2 Full synthesis. 13.3 SMIKe andMIKe. 13.4 Analysis of synthetic samples. 13.5 An application. 13.6 Conclusions. 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress, by Neal Thomas. 14.1 Introduction. 14.2 Statistical methods in NAEP. 14.3 Split and balanced designs for estimating population parameters. 14.4 Maximum likelihood estimation. 14.5 The role of secondary covariates. 14.6 Conclusions. 15 Propensity score estimation with missing data, by Ralph B. D’Agostino Jr. 15.1 Introduction. 15.2 Notation. 15.3 Applied example:March of Dimes data. 15.4 Conclusion and future directions. 16 Sensitivity to nonignorability in frequentist inference, by Guoguang Ma and Daniel F. Heitjan. 16.1 Missing data in clinical trials. 16.2 Ignorability and bias. 16.3 A nonignorable selection model. 16.4 Sensitivity of the mean and variance. 16.5 Sensitivity of the power. 16.6 Sensitivity of the coverage probability. 16.7 An example. 16.8 Discussion. III Statistical modeling and computation. 17 Statistical modeling and computation, by D. Michael Titterington. 17.1 Regression models. 17.2 Latent-variable problems. 17.3 Computation: non-Bayesian. 17.4 Computation: Bayesian. 17.5 Prospects for the future. 18 Treatment effects in before-after data, by Andrew Gelman. 18.1 Default statistical models of treatment effects. 18.2 Before-after correlation is typically larger for controls than for treated units. 18.3 A class of models for varying treatment effects. 18.4 Discussion. 19 Multimodality in mixture models and factor models, by Eric Loken. 19.1 Multimodality in mixture models. 19.2 Multimodal posterior distributions in continuous latent variable models. 19.3 Summary. 20 Modeling the covariance and correlation matrix of repeated measures, by W. John Boscardin and Xiao Zhang. 20.1 Introduction. 20.2 Modeling the covariance matrix. 20.3 Modeling the correlation matrix. 20.4 Modeling a mixed covariance-correlation matrix. 20.5 Nonzero means and unbalanced data. 20.6 Multivariate probit model. 20.7 Example: covariance modeling. 20.8 Example: mixed data. 21 Robit regression: a simple robust alternative to logistic and probit regression, by Chuanhai Liu. 21.1 Introduction. 21.2 The robit model. 21.3 Robustness of likelihood-based inference using logistic, probit, and robit regression models. 21.4 Complete data for simple maximum likelihood estimation. 21.5 Maximum likelihood estimation using EM-type algorithms. 21.6 A numerical example. 21.7 Conclusion. 22 Using EM and data augmentation for the competing risks model, by Radu V. Craiu and Thierry Duchesne. 22.1 Introduction. 22.2 The model. 22.3 EM-based analysis. 22.4 Bayesian analysis. 22.5 Example. 22.6 Discussion and further work. 23 Mixed effects models and the EM algorithm, by Florin Vaida, Xiao-Li Meng, and Ronghui Xu. 23.1 Introduction. 23.2 Binary regression with random effects. 23.3 Proportional hazards mixed-effects models. 24 The sampling/importance resampling algorithm, by Kim-Hung Li. 24.1 Introduction. 24.2 SIR algorithm. 24.3 Selection of the pool size. 24.4 Selection criterion of the importance sampling distribution. 24.5 The resampling algorithms. 24.6 Discussion. IV Applied Bayesian inference. 25 Whither applied Bayesian inference?, by Bradley P. Carlin. 25.1 Where we’ve been. 25.2 Where we are. 25.3 Where we’re going. 26 Efficient EM-type algorithms for fitting spectral lines in high-energy astrophysics, by David A. van Dyk and Taeyoung Park. 26.1 Application-specific statistical methods . 26.2 The Chandra X-ray observatory. 26.3 Fitting narrow emission lines. 26.4 Model checking and model selection. 27 Improved predictions of lynx trappings using a biological model, by Cavan Reilly and Angelique Zeringue. 27.1 Introduction. 27.2 The current best model. 27.3 Biological models for predator prey systems. 27.4 Some statistical models based on the Lotka-Volterra system. 27.5 Computational aspects of posterior inference. 27.6 Posterior predictive checks and model expansion. 27.7 Prediction with the posterior mode. 27.8 Discussion. 28 Record linkage using finite mixture models, by Michael D. Larsen. 28.1 Introduction to record linkage. 28.2 Record linkage. 28.3 Mixture models. 28.4 Application. 28.5 Analysis of linked files. 28.6 Bayesian hierarchical record linkage. 28.7 Summary. 29 Identifying likely duplicates by record linkage in a survey of prostitutes, by Thomas R. Belin, Hemant Ishwaran, Naihua Duan, Sandra H. Berry, and David E. Kanouse. 29.1 Concern about duplicates in an anonymous survey. 29.2 General frameworks for record linkage. 29.3 Estimating probabilities of duplication in the Los Angeles Women’s Health Risk Study. 29.4 Discussion. 30 Applying structural equation models with incomplete data, by Hal S. Stern and Yoonsook Jeon. 30.1 Structural equation models. 30.2 Bayesian inference for structural equation models. 30.3 Iowa Youth and Families Project example. 30.4 Summary and discussion. 31 Perceptual scaling, by Ying Nian Wu, Cheng-En Guo, and Song Chun Zhu. 31.1 Introduction. 31.2 Sparsity and minimax entropy. 31.3 Complexity scaling law. 31.4 Perceptibility scaling law. 31.5 Texture = imperceptible structures. 31.6 Perceptibility and sparsity. References. Index.

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Book SynopsisFundamentals of Computational Swarm Intelligence provides a comprehensive introduction to the new computational paradigm of Swarm Intelligence (SI), a field that emerged from biological research, and is now picking up momentum within the computational research community.Table of ContentsList of Tables. List of Figures. List of Algorithms. Preface. 1. Introduction. PART I: OPTIMIZATION THEORY. 2. Optimization Problems and Methods. 2.1 Basic ingredients of optimization problems. 2.2 Optimization problem classifications. 2.3 Optimality conditions. 2.4 Optimization method classes. 2.5 General conditions for convergence. 2.6 Summary. 3. Unconstrained Optimization. 3.1 Problem definition. 3.2 Optimization algorithms. 3.3 Example benchmark problems. 3.4 Summary. 4. Constrained Optimization. 4.1 Definition. 4.2 Constraint handling methods. 4.3 Example benchmark problems. 4.4 Summary. 5. Multi-solution Problems. 5.1 Definition. 5.2 Niching algorithm categories. 5.3 Example benchmark problems. 5.4 Summary. 6. Multi-objective Optimization. 6.1 Multi-objective problem. 6.2 Pareto-optimality. 6.3 Summary. 7. Dynamic Optimization Problems. 7.1 Definition. 7.2 Dynamic environment types. 7.3 Example benchmark problems. 7.4 Summary. PART II: EVOLUTIONARY COMPUTATION. 8. Introduction to Evolutionary Computation. 8.1 General evolutionary algorithm. 8.2 Representation. 8.3 Initial population. 8.4 Fitness function. 8.5 Selection. 8.6 Reproduction operators. 8.7 Evolutionary computation versus classical optimization. 8.8 Summary. 9. Evolutionary Computation Paradigms. 9.1 Genetic algorithms. 9.2 Genetic programming. 9.3 Evolutionary programming. 9.4 Evolution strategies. 9.5 Differential evolution. 9.6 Cultural algorithms. 9.7 Summary. 10. Coevolution. 10.1 Competitive coevolution. 10.2 Cooperative coevolution. 10.3 Summary. PART III: PARTICLE SWARM OPTIMIZATION. 11. Introduction. 12. Basic Swarm Optimization. 12.1 Full PSO model. 12.2 Social network structures. 12.3 Basic variations. 12.4 Basic PSO parameters. 12.5 Performance measures. 12.6 PSO versus EC. 12.7 Summary. 13. Particle Trajectories. 13.1 Convergence. 13.2 Surfing the waves. 13.3 Swarm equilibrium. 13.4 Constricted trajectories. 13.5 Unconstricted trajectories. 13.6 Parameter selection heuristics. 13.7 Summary. 14. Convergence Proofs. 14.1 Convergence proof for basic PSO. 14.2 PSO with guaranteed local convergence. 14.3 Global convergence of PSO. 14.4 Summary. 15. Single-Solution Particle Swarm Optimization. 15.1 Social based PSO algorithms. 15.2 Hybrid algorithms. 15.3 Sub-swarm-based PSO. 15.4 Memetic PSO algorithms. 15.5 Multi-start PSO algorithms. 15.6 Repelling methods. 15.7 Summary. 16. Niching with Particle Swarm Optimization. 16.1 Niching capability of basic PSO. 16.2 Sequential PSO niching. 16.3 Parallel PSO niching. 16.4 Quasi-sequential niching. 16.5 Performance measures. 16.6 Summary. 17. Constrained Optimization Using Particle Swarm Optimization. 17.1 Reject infeasible solutions. 17.2 Penalty function methods. 17.3 Convert to unconstrained problems. 17.4 Repair methods. 17.5 Preserving feasibility methods. 17.6 Pareto ranking methods. 17.7 Boundary constraints. 17.8 Applications. 17.9 Summary. 18. Multi-Objective Optimization with Particle Swarms. 18.1 Objectives of MOO. 18.2 Basic PSO versus MOO. 18.3 Aggregation-based methods. 18.4 Criterion-based methods. 18.5 Dominance-based methods. 18.6 Performance measures. 18.7 Summary. 19. Dynamic Environments with Particle Swarm Optimization. 19.1 Consequences for PSO. 19.2 PSO solutions for dynamic environments. 19.3 Performance measurement in dynamic environments. 19.4 Applications of PSO to dynamic problems. 19.5 Summary. 20. Discrete Particle Swarm Optimization. 20.1 Binary PSO. 20.2 General Discrete PSO. 20.3 Example applications. 20.4 Design of combinational circuits. 20.5 Summary. 21. Particle Swarm Optimization Applications. 21.1 Neural networks. 21.2 Game learning. 21.3 Clustering applications. 21.4 Design applications. 21.5 Scheduling and planning applications. 21.6 Controllers applications. 21.7 Applied mathematics. 21.8 Applications in power systems. 21.9 Miscellaneous applications. 21.10 Summary. PART VI: ANT ALGORITHMS. 22. Introduction. 23. Ant Colony Optimization Meta-Heuristic. 23.1 Foraging behaviour of ants. 23.2 Simple ant colony optimization. 23.3 Early ant algorithms. 23.4 Parameter settings. 23.5 Summary. 24. General Frameworks for Ant Colony Optimization Algorithms. 24.1 ACO algorithms characteristics. 24.2 Generic frameworks. 24.3 Summary. 25. Ant Colony Optimization Algorithms. 25.1 Single colony ACO algorithms. 25.2 Continuous ACO. 25.3 Multiple colony algorithms. 25.4 Hybrid ACO algorithms. 25.5 Multi-objective optimization. 25.6 Dynamic optimization problems. 25.7 Parallel ACO algorithms. 25.8 Summary. 26. Ant Colony Optimization Applications. 26.1 General requirements. 26.2 Ordering problems. 26.3 Assignment problems. 26.4 Subset problems. 26.5 Grouping problems. 26.6 Summary. 27. Collective Decision-Making. 27.1 Stigmergy. 27.2 Artificial Pheromone. 27.3 Heterarchy. 27.4 Summary. 28. Ant Colony Optimization Convergence. 28.1 Convergence proofs and characteristics. 28.2 Convergence measures. 28.3 Summary. 29. Cemetery Organisation and Brood Care. 29.1 Basic ant colony clustering model. 29.2 Generalized ant colony clustering model. 29.3 Minimal model for ant clustering. 29.4 Ant clustering ensemble. 29.5 Hybrid clustering approaches. 29.6 Ant clustering applications. 29.7 Summary. 30. Division of Labor. 30.1 Division of labor in insect colonies. 30.2 Task allocation based on response thresholds. 30.3 Adaptive task allocation and specialization. 30.4 Summary. 31. Final Remarks. References. Further Reading. Appendix A: Acronyms. Appendix B: Symbols. B.1 Part I - Optimization Theory. B.2 Part II - Evolutionary Computation. B.3 Part III - Particle Swarm Optimization. B.4 Part IV - Ant Algorithms. Index.

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Book SynopsisThe use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike.Trade Review"…a good book on the shelves of researchers in categorical data analysis." (Technometrics, May 2007) "…valuable for anyone interested in how Bayesian ideas apply in practice an should prove useful for anyone using the WINBUGS package for categorical data analysis." (Biometrics, March 2007) "…an excellent resource for biostatisticians and medical researchers." (Doody's Health Services) "…perfectly suited as a reference for any practitioner….Congdon has done a laudable job of introducing jointly the concepts of categorical data and Bayesian analysis." (Journal of the American Statistical Association, June 2006) "The author’s clear and logical approach makes the book accessible" (Zentralblatt MATH Volume 1079)Table of ContentsPreface. Chapter 1 Principles of Bayesian Inference. 1.1 Bayesian updating. 1.2 MCMC techniques. 1.3 The basis for MCMC. 1.4 MCMC sampling algorithms. 1.5 MCMC convergence. 1.6 Competing models. 1.7 Setting priors. 1.8 The normal linear model and generalized linear models. 1.9 Data augmentation. 1.10 Identifiability. 1.11 Robustness and sensitivity. 1.12 Chapter themes. References. Chapter 2 Model Comparison and Choice. 2.1 Introduction: formal methods, predictive methods and penalized deviance criteria. 2.2 Formal Bayes model choice. 2.3 Marginal likelihood and Bayes factor approximations. 2.4 Predictive model choice and checking. 2.5 Posterior predictive checks. 2.6 Out-of-sample cross-validation. 2.7 Penalized deviances from a Bayes perspective. 2.8 Multimodel perspectives via parallel sampling. 2.9 Model probability estimates from parallel sampling. 2.10 Worked example. References. Chapter 3 Regression for Metric Outcomes. 3.1 Introduction: priors for the linear regression model. 3.2 Regression model choice and averaging based on predictor selection. 3.3 Robust regression methods: models for outliers. 3.4 Robust regression methods: models for skewness and heteroscedasticity. 3.5 Robustness via discrete mixture models. 3.6 Non-linear regression effects via splines and other basis functions. 3.7 Dynamic linear models and their application in non-parametric regression. Exercises. References. Chapter 4; Models for Binary and Count Outcomes. 4.1 Introduction: discrete model likelihoods vs. data augmentation. 4.2 Estimation by data augmentation: the Albert–Chib method. 4.3 Model assessment: outlier detection and model checks. 4.4 Predictor selection in binary and count regression. 4.5 Contingency tables. 4.6 Semi-parametric and general additive models for binomial and count responses. Exercises. References. Chapter 5 Further Questions in Binomial and Count Regression. 5.1 Generalizing the Poisson and binomial: overdispersion and robustness. 5.2 Continuous mixture models. 5.3 Discrete mixtures. 5.4 Hurdle and zero-inflated models. 5.5 Modelling the link function. 5.6 Multivariate outcomes. Exercises. References. Chapter 6 Random Effect and Latent Variable Models for Multicategory Outcomes. 6.1 Multicategory data: level of observation and relations between categories. 6.2 Multinomial models for individual data: modelling choices. 6.3 Multinomial models for aggregated data: modelling contingency tables. 6.4 The multinomial probit. 6.5 Non-linear predictor effects. 6.6 Heterogeneity via the mixed logit. 6.7 Aggregate multicategory data: the multinomial–Dirichlet model and extensions. 6.8 Multinomial extra variation. 6.9 Latent class analysis. Exercises. References. Chapter 7 Ordinal Regression. 7.1 Aspects and assumptions of ordinal data models. 7.2 Latent scale and data augmentation. 7.3 Assessing model assumptions: non-parametric ordinal regression and assessing ordinality. 7.4 Location-scale ordinal regression. 7.5 Structural interpretations with aggregated ordinal data. 7.6 Log-linear models for contingency tables with ordered categories. 7.7 Multivariate ordered outcomes. Exercises. References. Chapter 8Discrete Spatial Data. 8.1 Introduction. 8.2 Univariate responses: the mixed ICAR model and extensions. 8.3 Spatial robustness. 8.4 Multivariate spatial priors. 8.5 Varying predictor effect models. Exercises. References. Chapter 9 Time Series Models for Discrete Variables. 9.1 Introduction: time dependence in observations and latent data. 9.2 Observation-driven dependence. 9.3 Parameter-driven dependence via DLMs. 9.4 Parameter-driven dependence via autocorrelated error models. 9.5 Integer autoregressive models. 9.6 Hidden Markov models. Exercises. References. Chapter 10 Hierarchical and Panel Data Models 10.1 Introduction: clustered data and general linear mixed models. 10.2 Hierarchical models for metric outcomes. 10.3 Hierarchical generalized linear models. 10.4 Random effects for crossed factors. 10.5 The general linear mixed model for panel data. 10.6 Conjugate panel models. 10.7 Growth curve analysis. 10.8 Multivariate panel data. 10.9 Robustness in panel and clustered data analysis. 10.10 APC and spatio-temporal models. 10.11 Space–time and spatial APC models. Exercises. References. Chapter 11 Missing-Data Models. 11.1 Introduction: types of missing data. 11.2 Density mechanisms for missing data. 11.3 Auxiliary variables. 11.4 Predictors with missing values. 11.5 Multiple imputation. 11.6 Several responses with missing values. 11.7 Non-ignorable non-response models for survey tabulations. 11.8 Recent developments. Exercises. References. Index.

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Book SynopsisThis book provides an up--to--date, comprehensive, clinically oriented account of the molecular biology, pathogenesis, clinical features, diagnosis and management of human bacterial diseases, as well as their control and prevention.Trade Review“…a valuable resource for both practitioners and students alike.” (Journal of the Royal Statistical Society, Series A, Vol.168, No.2, March 2005)Table of ContentsPreface. Series Preface. 1. The Scope of Survival Analysis. 2. Randomized Clinical Trials: General Principles and Some Controversial Issues. 3. Estimation of Survival Probabilities. 4. Non-Parametric Methods for the Comparison of Survival Curves. 5. Distribution Functions for Failure Time T . 6. The Cox Regression Model. 7. Validation of the Proportional Hazards Models. 8. Parametric Regression Models. 9. The Study of Prognostic Factors and the Assessment of Treatment Effect. 10. Competing Risks. 11. Meta-Analysis. References. Author Index. Subject Index.

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Book SynopsisData Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems.Table of ContentsPreface. List of Contributors. SECTION I: INTRODUCTION - DATA DIVERSITY AND INTEGRATION. 1. Integrative Data Analysis and Visualization: Introduction to Critical Problems, Goals and Challenges (Francisco Azuaje and Joaquín Dopazo). 1.1 Data Analysis and Visualization: An Integrative Approach. 1.2 Critical Design and Implementation Factors. 1.3 Overview of Contributions. References. 2. Biological Databases: Infrastructure, Content and Integration (Allyson L. Williams, Paul J. Kersey, Manuela Pruess and Rolf Apweiler). 2.1 Introduction. 2.2 Data Integration. 2.3 Review of Molecular Biology Databases. 2.4 Conclusion. References. 3. Data and Predictive Model Integration: an Overview of Key Concepts, Problems and Solutions (Francisco Azuaje, Joaquín Dopazo and Haiying Wang). 3.1 Integrative Data Analysis and Visualization: Motivation and Approaches. 3.2 Integrating Informational Views and Complexity for Understanding Function. 3.3 Integrating Data Analysis Techniques for Supporting Functional Analysis. 3.4 Final Remarks. References. SECTION II: INTEGRATIVE DATA MINING AND VISUALIZATION -EMPHASIS ON COMBINATION OF MULTIPLE DATA TYPES. 4. Applications of Text Mining in Molecular Biology, from Name Recognition to Protein Interaction Maps (Martin Krallinger and Alfonso Valencia). 4.1 Introduction. 4.2 Introduction to Text Mining and NLP. 4.3 Databases and Resources for Biomedical Text Mining. 4.4 Text Mining and Protein-Protein Interactions. 4.5 Other Text-Mining Applications in Genomics. 4.6 The Future of NLP in Biomedicine. Acknowledgements. References. 5. Protein Interaction Prediction by Integrating Genomic Features and Protein Interaction Network Analysis (Long J. Lu, Yu Xia, Haiyuan Yu, Alexander Rives, Haoxin Lu, Falk Schubert and Mark Gerstein). 5.1 Introduction. 5.2 Genomic Features in Protein Interaction Predictions. 5.3 Machine Learning on Protein-Protein Interactions. 5.4 The Missing Value Problem. 5.5 Network Analysis of Protein Interactions. 5.6 Discussion. References. 6. Integration of Genomic and Phenotypic Data (Amanda Clare). 6.1 Phenotype. 6.2 Forward Genetics and QTL Analysis. 6.3 Reverse Genetics. 6.4 Prediction of Phenotype from Other Sources of Data. 6.5 Integrating Phenotype Data with Systems Biology. 6.6 Integration of Phenotype Data in Databases. 6.7 Conclusions. References. 7. Ontologies and Functional Genomics (Fátima Al-Shahrour and Joaquín Dopazo). 7.1 Information Mining in Genome-Wide Functional Analysis. 7.2 Sources of Information: Free Text Versus Curated Repositories. 7.3 Bio-Ontologies and the Gene Ontology in Functional Genomics. 7.4 Using GO to Translate the Results of Functional Genomic Experiments into Biological Knowledge. 7.5 Statistical Approaches to Test Significant Biological Differences. 7.6 Using FatiGO to Find Significant Functional Associations in Clusters of Genes. 7.7 Other Tools. 7.8 Examples of Functional Analysis of Clusters of Genes. 7.9 Future Prospects. References. 8. The C. elegans Interactome: its Generation and Visualization (Alban Chesnau and Claude Sardet). 8.1 Introduction. 8.2 The ORFeome: the first step toward the interactome of C. elegans. 8.3 Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans Protein-Protein Interaction (Interactome) Network: Technical Aspects. 8.4 Visualization and Topology of Protein-Protein Interaction Networks. 8.5 Cross-Talk Between the C. elegans Interactome and other Large-Scale Genomics and Post-Genomics Data Sets. 8.6 Conclusion: From Interactions to Therapies. References. SECTION III: INTEGRATIVE DATA MINING AND VISUALIZATION - EMPHASIS ON COMBINATION OF MULTIPLE PREDICTION MODELS AND METHODS. 9. Integrated Approaches for Bioinformatic Data Analysis and Visualization - Challenges, Opportunities and New Solutions (Steve R. Pettifer, James R. Sinnott and Teresa K. Attwood). 9.1 Introduction. 9.2 Sequence Analysis Methods and Databases. 9.3 A View Through a Portal. 9.4 Problems with Monolithic Approaches: One Size Does Not Fit All. 9.5 A Toolkit View. 9.6 Challenges and Opportunities. 9.7 Extending the Desktop Metaphor. 9.8 Conclusions. Acknowledgements. References. 10. Advances in Cluster Analysis of Microarray Data (Qizheng Sheng, Yves Moreau, Frank De Smet, Kathleen Marchal and Bart De Moor). 10.1 Introduction. 10.2 Some Preliminaries. 10.3 Hierarchical Clustering. 10.4 k-Means Clustering. 10.5 Self-Organizing Maps. 10.6 A Wish List for Clustering Algorithms. 10.7 The Self-Organizing Tree Algorithm. 10.8 Quality-Based Clustering Algorithms. 10.9 Mixture Models. 10.10 Biclustering Algorithms. 10.11 Assessing Cluster Quality. 10.12 Open Horizons. References. 11. Unsupervised Machine Learning to Support Functional Characterization of Genes: Emphasis on Cluster Description and Class Discovery (Olga G. Troyanskaya). 11.1 Functional Genomics: Goals and Data Sources. 11.2 Functional Annotation by Unsupervised Analysis of Gene Expression Microarray Data. 11.3 Integration of Diverse Functional Data For Accurate Gene Function Prediction. 11.4 MAGIC - General Probabilistic Integration of Diverse Genomic Data. 11.5 Conclusion. References. 12. Supervised Methods with Genomic Data: a Review and Cautionary View (Ramón Díaz-Uriarte). 12.1 Chapter Objectives. 12.2 Class Prediction and Class Comparison. 12.3 Class Comparison: Finding/Ranking Differentially Expressed Genes. 12.4 Class Prediction and Prognostic Prediction. 12.5 ROC Curves for Evaluating Predictors and Differential Expression. 12.6 Caveats and Admonitions. 12.7 Final Note: Source Code Should be Available. Acknowledgements. References. 13. A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models (Pedro Larrañaga, Iñaki Inza and Jose L. Flores). 13.1 Introduction. 13.2 Genetic Networks. 13.3 Probabilistic Graphical Models. 13.4 Inferring Genetic Networks by Means of Probabilistic Graphical Models. 13.5 Conclusions. Acknowledgements. References. 14. Integrative Models for the Prediction and Understanding of Protein Structure Patterns (Inge Jonassen). 14.1 Introduction. 14.2 Structure Prediction. 14.3 Classifications of Structures. 14.4 Comparing Protein Structures 14.5 Methods for the Discovery of Structure Motifs. 14.6 Discussion and Conclusions. References. Index.

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Book SynopsisThis book provides an overview of available methods for bioequivalence studies, adopting a practical approach via numerous examples using real data. All medical/pharmacokinetic background is provided, so that the book is suitable for both medical practitioners/pharmaceutical scientists, and biometricians.Trade Review"The book provides an excellent introduction for researchers approaching the concept of bioequivalence and is a complete and useful compendium for experienced statisticians." (Biometrical Journal, April 2009) "The book provides an important reference providing many worked examples with real data from drug development. Professionals from the harmaceutical industry and regulatory bodies will particularly appreciate the emphasis made on regulatory guidelines." (Statistical Methods in Medical Research, February 2009) "Bioequivalence Studies in Drug Development: Methods and Applications is an informative, timely, and easy-to-read contribution to bioequivalence and drug-drug/food-drug interaction literature." (Journal of the American Statistical Association, September 2008) "…those statisticians working in this area of research will find that this book will serve as an excellent reference for their work..." (Journal of Biopharmaceutical Statistics, January 2008) "This book would be beneficial to both pharmaceutical scientists/researchers and biostatisticians…" (Biometrics, September 2007) "For anyone interested in any aspect of bioequivalence, the book is a very valuable reference." (International Statistical Review, 2007) "…my pleasure to review…I would like to add this book to my book collection of pharmaceutical research and development." (Biometrics, September 2007)Table of ContentsPreface. 1 Introduction. 1.1 Definitions. 1.2 When are bioequivalence studies performed. 1.3 Design and conduct of bioequivalence studies. 1.4 Aims and structure of the book. References. 2 Metrics to characterize concentration-time profiles in single- and multiple-dose bioequivalence studies. 2.1 Introduction. 2.2 Pharmacokinetic characteristics (metrics) for single-dose studies. 2.3 Pharmacokinetic rate and extent characteristics (metrics) for multiple-dose studies. 2.4 Conclusions. References. 3 Basic statistical considerations. 3.1 Introduction. 3.2 Additive and multiplicative model. 3.3 Hypotheses testing. 3.4 The RT/TR crossover design assuming an additive model. References. 4 Assessment of average bioequivalence in the RT/TR design. 4.1 Introduction. 4.2 The RT/TR crossover design assuming a multiplicative model. 4.3 Test procedures for bioequivalence assessment. 4.4 Conclusions. References. 5 Power and sample size determination for testing average bioequivalence in the RT/TR design. 5.1 Introduction. 5.2 Challenging the classical approach. 5.3 Exact power and sample size calculation. 5.4 Modified acceptance ranges. 5.5 Approximate formulas for sample size calculation. 5.6 Exact power and sample size calculation by nQuery®. References. Appendix. 6 Presentation of bioequivalence studies. 6.1 Introduction. 6.2 Results from a single-dose study. 6.3 Results from a multiple-dose study. 6.4 Conclusions. References. 7 Designs with more than two formulations. 7.1 Introduction. 7.2 Williams designs. 7.3 Example: Dose linearity study. 7.4 Multiplicity. 7.5 Conclusions. References. 8 Analysis of pharmacokinetic interactions. 8.1 Introduction. 8.2 Pharmacokinetic drug-drug interaction studies. 8.3 Pharmacokinetic food-drug interactions. 8.4 Goal posts for drug interaction studies including no effect boundaries. 8.5 Labeling. 8.6 Conclusions. References. 9 Population and individual bioequivalence. 9.1 Introduction. 9.2 Brief history. 9.3 Study designs and statistical models. 9.4 Population bioequivalence. 9.5 Individual bioequivalence. 9.6 Disaggregate criteria. 9.7 Other approaches. 9.8 Average bioequivalence in replicate designs. 9.9 Example: The anti-hypertensive patch dataset. 9.10 Conclusions. References. 10 Equivalence assessment in case of clinical endpoints. 10.1 Introduction. 10.2 Design and testing procedure. 10.3 Power and sample size calculation. 10.4 Conclusions. Apendix. References. Index.

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Book SynopsisApplied Likelihood Methods provides an accessible and practical introduction to likelihood modeling, supported by examples and software. The book features applications from a range of disciplines, including statistics, medicine, biology, and ecology.Trade Review“This book is well-presented and would suit applied scientists, researchers, graduate students and particularly anyone who uses likelihood and such methods to their studies and applications.” (ISR, 2012) Table of ContentsPreface xiii Part I PRELIMINARIES 1 1 A taste of likelihood 3 1.1 Introduction 3 1.2 Motivating example 4 1.3 Using SAS, R and ADMB 9 1.4 Implementation of the motivating example 11 1.5 Exercises 17 2 Essential concepts and iid examples 18 2.1 Introduction 18 2.2 Some necessary notation 19 2.3 Interpretation of likelihood 23 2.4 IID examples 25 2.5 Exercises 33 Part II PRAGMATICS 37 3 Hypothesis tests and confidence intervals or regions 39 3.1 Introduction 39 3.2 Approximate normality of MLEs 40 3.3 Wald tests, confidence intervals and regions 43 3.4 Likelihood ratio tests, confidence intervals and regions 49 3.5 Likelihood ratio examples 54 3.6 Profile likelihood 57 3.7 Exercises 59 4 What you really need to know 64 4.1 Introduction 64 4.2 Inference about g(θ) 65 4.3 Wald statistics – quick and dirty? 75 4.4 Model selection 79 4.5 Bootstrapping 81 4.6 Prediction 91 4.7 Things that can mess you up 95 4.8 Exercises 98 5 Maximizing the likelihood 101 5.1 Introduction 101 5.2 The Newton-Raphson algorithm 103 5.3 The EM (Expectation–Maximization) algorithm 104 5.4 Multi-stage maximization 113 5.5 Exercises 118 6 Some widely used applications of maximum likelihood 121 6.1 Introduction 121 6.2 Box-Cox transformations 122 6.3 Models for survival-time data 125 6.4 Mark–recapture models 134 6.5 Exercises 141 7 Generalized linear models and extensions 143 7.1 Introduction 143 7.2 Specification of a GLM 144 7.3 Likelihood calculations 148 7.4 Model evaluation 149 7.5 Case study 1: Logistic regression and inverse prediction in R 154 7.6 Beyond binomial and Poisson models 161 7.7 Case study 2: Multiplicative vs additive models of over-dispersed counts in SAS 167 7.8 Exercises 173 8 Quasi-likelihood and generalized estimating equations 175 8.1 Introduction 175 8.2 Wedderburn’s quasi-likelihood 177 8.3 Generalized estimating equations 181 8.4 Exercises 187 9 ML inference in the presence of incidental parameters 188 9.1 Introduction 188 9.2 Conditional likelihood 192 9.3 Integrated likelihood 198 9.3.1 Justification 199 9.3.2 Uses of integrated likelihood 200 9.4 Exercises 201 10 Latent variable models 202 10.1 Introduction 202 10.2 Developing the likelihood 203 10.3 Software 204 10.4 One-way linear random-effects model 210 10.5 Nonlinear mixed-effects model 217 10.6 Generalized linear mixed-effects model 221 10.7 State-space model for count data 227 10.8 ADMB template files 228 10.9 Exercises 232 Part III THEORETICAL FOUNDATIONS 233 11 Cramer-Rao inequality and Fisher information 235 11.1 Introduction 235 11.2 The Cramer-Rao inequality for θ RI 236 11.3 Cramer-Rao inequality for functions of θ 239 11.4 Alternative formulae for I (θ) 241 11.5 The iid data case 243 11.6 The multi-dimensional case, θ RI s 243 11.7 Examples of Fisher information calculation 247 11.8 Exercises 253 12 Asymptotic theory and approximate normality 256 12.1 Introduction 256 12.2 Consistency and asymptotic normality 257 12.3 Approximate normality 271 12.4 Wald tests and confidence regions 276 12.5 Likelihood ratio test statistic 280 12.6 Rao-score test statistic 281 12.7 Exercises 283 13 Tools of the trade 286 13.1 Introduction 286 13.2 Equivalence of tests and confidence intervals 286 13.3 Transformation of variables 287 13.4 Mean and variance conditional identities 288 13.5 Relevant inequalities 289 13.6 Asymptotic probability theory 291 13.7 Exercises 297 14 Fundamental paradigms and principles of inference 299 14.1 Introduction 299 14.2 Sufficiency principle 300 14.3 Conditionality principle 304 14.4 The likelihood principle 306 14.5 Statistical significance versus statistical evidence 309 14.6 Exercises 311 15 Miscellanea 313 15.1 Notation 313 15.2 Acronyms 315 15.3 Do you think like a frequentist or a Bayesian? 315 15.4 Some useful distributions 316 15.5 Software extras 321 15.6 Automatic differentiation 323 Appendix: Partial solutions to selected exercises 325 Bibliography 337 Index 345

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Book SynopsisThis valuable book-length treatment of the field offers coverage of estimation for situations where the model variables are observed subject to measurement error. Included are regression models with errors in the variables, latent variable models, and factor models.Trade Review"…very interesting for the theory it contains… enjoyable and useful." (Zentralblatt MATH, 1107, 64)Table of ContentsList of Examples. List of Principal Results. List of Figures. 1. A Single Explanatory Variable. 2. Vector Explanatory Variables. 3. Extensions of the Single Relation Model. 4. Multivariate Models. Bibliography. Author Index. Subject Index.

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Book SynopsisThe essentials of point-set topology, complete with motivation and numerous examples Topology: Point-Set and Geometric presents an introduction to topology that begins with the axiomatic definition of a topology on a set, rather than starting with metric spaces or the topology of subsets of Rn. This approach includes many more examples, allowing students to develop more sophisticated intuition and enabling them to learn how to write precise proofs in a brand-new context, which is an invaluable experience for math majors. Along with the standard point-set topology topicsconnected and path-connected spaces, compact spaces, separation axioms, and metric spacesTopology covers the construction of spaces from other spaces, including products and quotient spaces. This innovative text culminates with topics from geometric and algebraic topology (the Classification Theorem for Surfaces and the fundamental group), which provide instructors with the opportunity to choose which capstoneTrade Review"Ideally suited for an introductory course in topology at the junior/senior level." (CHOICE, September 2007)Table of ContentsForeword. Acknowledgments. 1. Introduction: Intuitive Topology. 2. Background on Sets and Functions. 3. Topological Spaces. 4. More on Open and Closed Sets and Continuous Functions. 5. New Spaces from Old. 6. Connected Spaces. 7. Compact Spaces. 8. Separation Axioms. 9. Metric Spaces. 10. The Classification of Surfaces. 11. Fundamental Groups and Covering Spaces. References. Index.

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Book SynopsisOver the years, I have had the opportunity to teach several regression courses, and I cannot think of a better undergraduate text than this one.The American Statistician The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite. I also highly recommend it to practitioners who want to solve real-life prediction problems. (Computing Reviews) Modern Regression Methods, Second Edition maintains the accessible organization, breadth of coverage, and cutting-edge appeal that earned its predecessor the title of being one of the top five books for statisticians by an Amstat News book editor in 2003. This new edition has been updated and enhanced to include all-new information on the latest advances and research in the evolving field of regression analysis. The book provides a unique treatment of fundamental regression methods, suTrade Review"The book is to be praised in that it makes the reader aware of a large number of approaches to regression situations, and also to their possible pitfalls. It is thus an excellent basis for an experienced instructor to teach regression at different levels." (Springer, August 2010) "This book, at the undergraduate level and even at the graduate level, will be rewarding reading for anyone interested in learning the nuances of regression analysis." (Mathmatical Reviews, January 2010) "The exercises are interesting and thought-provoking throughout. If you liked the first edition, you will be pleased with this revision also." (International Statistical Review, August 2009) "The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite. I also highly recommend it to practitioners who want to solve real-life prediction problems." (Computing Reviews, July 2009) "In this second edition, Ryan (author, editor, and educator) provides substantial updates and revisions of his popular text for statisticians to include new information on the most current advances and research in regression analysis" (SciTech Reviews, March 2009) "One would be hard-pressed to find another text that rivals this one in terms of coverage of the regression literature." (The American Statistician, 2009) "I strongly recommend the book as a reference for anyone teaching or using regression." (MAA Reviews, 2009) "Highly recommended for those already trained in mathematics and statistics who want a good guide to current practice and issues in multiple regression techniques." (Journal of Biopharmaceutical Statistics, 2009)Table of ContentsDiagnostics and Remedial Measures. Regression with Matrix Algebra. Introduction to Multiple Linear Regression. Plots in Multiple Regression. Transformations in Multiple Regression. Selection of Regressors. Polynomial and Trigonometric Terms. Logistic Regression. Nonparametric Regression. Robust Regression. Ridge Regression. Nonlinear Regression. Experimental Designs for Regression. Applications of Regression. Appendices. Index.

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Book SynopsisPractice calculus with this solutions manual For students using Calculus: One and Several Variables for classroom instruction, this complete solutions manual for chapters 1-12 provides the answer key to the one-variable problems presented in the text. Now in its tenth edition, Calculus: One and Several Variables has become known for its easy-to-understand writing style and balance of theory and application. With this solutions manual, students can apply their knowledge using the problems presented in the first 12 chapters and check their work as they go.Table of ContentsCHAPTER 1 1 CHAPTER 2 20 CHAPTER 3 37 CHAPTER 4 63 CHAPTER 5 125 CHAPTER 6 157 CHAPTER 7 186 CHAPTER 8 220 CHAPTER 9 263 CHAPTER 10 288 CHAPTER 11 322 CHAPTER 12 346

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Book SynopsisA self-contained introduction to the fundamentals of mathematical analysis Mathematical Analysis: A Concise Introduction presents the foundations of analysis and illustrates its role in mathematics.Trade Review"This highly original, interesting and very useful book includes over 900 exercises which are ranging in levels of difficulty, from conceptual questions and adaptations of proofs to proofs with and without hints." (Mathematical Reviews, 2008h)Table of ContentsPreface xi Part I: Analysis of Functions of a Single Real Variable 1 The Real Numbers 1 1.1 Field Axioms 1 1.2 Order Axioms 4 1.3 Lowest Upper and Greatest Lower Bounds 8 1.4 Natural Numbers, Integers, and Rational Numbers 11 1.5 Recursion, Induction, Summations, and Products 17 2 Sequences of Real Number V 25 2.1 Limits 25 2.2 Limit Laws 30 2.3 Cauchy Sequences 36 2.4 Bounded Sequences 40 2.5 Infinite Limits 44 3 Continuous Functions 49 3.1 Limits of Functions 49 3.2. Limit Laws 52 3.3 One-Sided Limits and Infinite Limits 56 3.4 Continuity 59 3.5 Properties of Continuous Functions 66 3.6 Limits at Infinity 69 4 Differentiable Functions 71 4.1 Differentiability 71 4.2 Differentiation Rules 74 4.3 Rolle's Theorem and the Mean Value Theorem 80 5 The Riemann Integral I 85 5.1 Riemann Sums and the Integral 85 5.2 Uniform Continuity and Integrability of Continuous Functions 91 5.3 The Fundamental Theorem of Calculus 95 5.4 The Darboux Integral 97 6 Series of Real Numbers I 101 6.1 Series as a Vehicle to Define Infinite Sums 101 6.2 Absolute Convergence and Unconditional Convergence 108 7 Some Set Theory 117 7.1 The Algebra of Sets 117 7.2 Countable Sets 122 7.3 Uncountable Sets 124 8 The Riemann Integral II 127 8.1 Outer Lebesgue Measure 127 8.2 Lebesgue's Criterion for Riemann Integrability 131 8.3 More Integral Theorems 136 8.4 Improper Riemann Integrals 140 9 The Lebesgue Integral 145 9.1 Lebesgue Measurable Sets 147 9.2 Lebesgue Measurable Functions 153 9.3 Lebesgue Integration 158 9.4 Lebesgue Integrals versus Riemann Integrals 165 10 Series of Real Numbers II 169 10.1 Limits Superior and Inferior 169 10.2 The Root Test and the Ratio Test 172 10.3 Power Series 175 11 Sequences of Functions 179 11.1 Notions of Convergence 179 11.2 Uniform Convergence 182 12 Transcendental Functions 189 12.1 The Exponential Function 189 12.2 Sine and Cosine 193 12.3 L.' Hôpital's Rule 199 13 Numerical Methods 203 13.1 Approximation with Taylor Polynomials 204 13.2 Newton's Method 208 13.3 Numerical Integration 214 Part II: Analysis in Abstract Spaces 14 Integration on Measure Spaces 225 14.1 Measure Spaces 225 14.2 Outer Measures 230 14.3 Measurable Functions 234 14.4 Integration of Measurable Functions 235 14.5 Monotone and Dominated Convergence 238 14.6 Convergence in Mean, in Measure, and Almost Everywhere 242 14.7 Product Ϭ-Algebras 245 14.8 Product Measures and Fubini's Theorem 251 15 The Abstract Venues for Analysis 255 15.1 Abstraction I: Vector Spaces 255 15.2 Representation of Elements; Bases and Dimension 259 15.3 Identification of Spaces: Isomorphism 262 15.4 Abstraction II: Inner Product Spaces 264 15.5 Nicer Representations: Orthonormal Sets 267 15.6 Abstraction III: Norrned Spaces 269 15.7 Abstraction IV: Metric Spaces 275 15.8 LP Spaces 278 15.9 Another Number Field: Complex Numbers 281 16 The Topology of Metric Spaces 287 16.1 Convergence of Sequences 287 16.2 Completeness 291 16.3 Continuous Functions 296 16.4 Open and Closed Sets 301 16.5 Compactness 309 16.6 The Normed Topology of Rd 316 16.7 Dense Subspaces 322 16.8 Connectedness 330 16.9 Locally Compact Spaces 333 17 Differentiation in Normed Spaces 341 17.1 Continuous Linear Functions 342 17.2 Matrix Representation of Linear Functions 348 17.3 Differentiability 353 17.4 The Mean Value Theorem 360 17.5 How Partial Derivatives Fit In 362 17.6 Multilinear Functions (Tensors) 369 17.7 Higher Derivatives 373 17.8 The. Implicit Function Theorem 380 18 Measure, Topology, and Differentiation 385 18.1 Lebesgue Measurable Sets in Rd 385 18.2 Cꝏ and Approximation of Integrable Functions 391 18.3 Tensor Algebra and Determinants 397 18.4 Multidimensional Substitution 407 19 Introduction to Differential Geometry 421 19.1 Manifolds 421 19.2 Tangent Spaces and Differentiable Functions 427 19.3 Differential Forms, Integrals Over the Unit Cube 434 19.4 k-Forms and Integrals Over k-Chains 443 19.5 Integration on Manifolds 452 19.6 Stokes' Theorem 458 20 Hilbert Spaces 463 20.1 Orthonormal Bases 463 20.2 Fourier Series 467 20.3 The Riesz Representation Theorem 475 Part III: Applied Analysis 21 Physics Background 483 21.1 Harmonic Oscillators 484 21.2 Heat and Diffusion 486 21.3 Separation of Variables, Fourier Series, and Ordinary Differential Equa-tions 490 21.4 Maxwell's Equations 493 21.5 The Navier Stokes Equation for the Conservation of Mass 496 22 Ordinary Differential Equations 505 22.1 Burwell Space Valued Differential Equations 505 22.2 An Existence and Uniqueness Theorem 508 22.3 Linear Differential Equations 510 23 The Finite Element Method 513 23.1 Ritz-Galerkin Approximation 513 23.2 Wealth Differentiable Functions 518 23,3 Sobolev Spaces 524 23.4 Elliptic Differential Operators 532 23.5 Finite Elements 536 Conclusion and Outlook 544 Appendices A Logic 545 A.1 Statements 545 A.2 Negations 546 B Set Theory 547 B. 1 The Zermelo-Fraenkel Axioms 547 B.2 Relations and Functions 548 C Natural Numbers, Integers, and Rational Numbers 549 C.1 The Natural Numbers 549 C.2 The Integers 550 C.3 The Rational Numbers 550 Bibliography 551 Index 553

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Book SynopsisDuring the last decades long-memory processes have evolved as a vital and important part of time series analysis. This book attempts to give an overview of the theory and methods developed to deal with long-range dependent data as well as describe some applications of these methodologies to real-life time series.Trade Review"...Palma presents a textbook for a graduate course summarizing the theory and methods developed to deal with long-range-dependent data, and describing some applications to real-life time series." (SciTech Book Reviews, June 2007) "...textbook for a graduate course summarizing the theory and methods developed to deal with long-range-dependent data, and describing some applications to real-life time series.... Problems and bibliographic notes are provided at the end of each chapter." (SciTech Book News, June 2007) "I believe that this text provides an important contribution to the long-memory time series literature. I feel that it largely achieves its aims and could be useful for those instructors wishing to teach a semester-long special topics course.... I strongly recommend this book to anyone interested in long-memory time series. Both researchers and beginners alike will find this text extremely useful." (Journal of the American Statisticial Association, Dec 2008) "Very well-organized catalogue of long-memory time series analysis." (Mathematical Reviews, 2008) "Judging by its contents and scope [the aim of this book] has been largely achieved.... The list of references is selective but quite comprehensive. Each chapter concludes with a 'Problems' section which should be helpful to instructors wishing to use this book as standalone basis for a course in its subject area..." (International Statistical Review, 2007)Table of ContentsPreface xiii Acronyms xvii 1 Stationary Precedes 1 1.1 Fundamental concepts 2 1.1.1 Stationarity 4 1.1.2 Singularity and Regularity 5 1.1.3 Wold Decomposition Theorem 5 1.1.4 Causality 7 1.1.5 Invertibility 7 1.1.6 Best Linear Predictor 8 1.1.7 Szego-Kolmogorov Formula 8 1.1.8 Ergodicity 9 1.1.9 Martingales 11 1.1.10 Cumulants 12 1.1.11 Fractional Brownian Motion 12 1.1.12 Wavelets 14 1.2 Bibliographic Notes 15 Problems 16 2 State Space Systems 21 2.1 Introduction 22 2.1.1 Stability 22 2.1.2 Hankel Operator 22 2.1.3 Observability 23 2.1.4 Controllability 23 2.1.5 Minimality 24 2.2 Representations of Linear Processes 24 2.2.1 State Space Form to Wold Decomposition 24 2.2.2 Wold Decomposition to State Form 25 2.2.3 Hankel Operator to State Space Form 25 2.3 Estimation of the State 26 2.3.1 State Predictor 27 2.3.2 State Filter 27 2.3.3 State Smoother 27 2.3.4 Missing Observation 28 2.3.5 Steady State System 28 2.3.6 Prediction of Future Observations 30 2.4 Extensions 32 2.5 Bibliographic Notes 32 Problems 33 3 Long-Memory/Processes 39 3.1 Defining Long Memory 40 3.1.1 Alternative Definitions 41 3.1.2 Extensions 43 3.2 ARFIMA Processes 43 3.2.1 Stationarity, Causality, and Invertibility 44 3.2.2 Infinite AR and MA Expansions 46 3.2.3 Spectral Density 47 3.2.4 Autocovariance Function 47 3.2.5 Sample Mean 48 3.2.6 Partial Autocorrelations 49 3.2.7 Illustrations 49 3.2.8 Approximation of Long-Memory Processes 55 3.3 Fractional Gaussian Noise 56 3.3.1 Sample Mean 56 3.4 Technical Lemmas 57 3.5 Bibliographic Notes 58 Problems 59 4 Estimation Methods 65 4.1 Maximum-Likelihood Estimation 66 4.1.1 Cholesky Decomposition Method 66 4.1.2 Durbin-Levinson Algorithm 66 4.1.3 Computation of Autocovariances 67 4.1.4 State Space Approach 69 4.2 Autoregressive Approximations 71 4.2.1 Haslett-Raftery Method72 4.2.2 Beran Approach 73 4.2.3 A State Space Method 74 4.3 Moving-Average Approximation 75 4.4 Whittle Estimation 78 4.4.1 Other versions 80 4.4.2 Non-Gaussian Data 80 4.4.3 Semiparametric Methods 81 4.5 Other Methods 81 4.5.1 A Regression Method 82 4.5.2 Rescale Range Method 83 4.5.3 Variance Plots 85 4.5.4 Detrended Fluctuation Analysis 87 4.5.5 A Wavelet-Based Method 91 4.6 Numerical Experiments 92 4.7 Bibliographic Notes 93 Problems 94 5 Asymptotic Theory 97 5.1 Notation and Definitions 98 5.2 Theorems 99 5.2.1 Consistency 99 5.2.2 Central Limit Theorem 101 5.2.3 Efficiency 104 5.3 Examples 104 5.4 Illustration 108 5.5 Technical Lemmas 109 5.6 Bibliographic Notes 109 Problems 109 6 Heteroskedastic Models 115 6.1 Introduction 116 6.2 ARFIMA-GARCH Model 117 6.2.1 Estimation 119 6.3 Other Models 119 6.3.1 Estimation 121 6.4 Stochastic Volatility 121 6.4.1 Estimation 122 6.5 Numerical Experiments 122 6.6 Application 123 6.6.1 Model without Leverage 123 6.6.2 Model with Leverage 124 6.6.3 Model Comparison 124 6.7 Bibliographic Notes 125 Problems 126 7 Transformations 131 7.1 Transformation of Gaussian Processes 132 7.2 Autocorrelation of Squares 134 7.3 Asymptotic behavior 136 7.4 Illustrations 138 7.5 Bibliographic Notes 142 Problems 143 8 Bayesian Methods 147 8.1 Bayesian Modeling 148 8.2 Markov Chain Monte Carlo Methods 149 8.2.1 Metropolis-Hastings Algorithm 149 8.2.2 Gibbs Sampler 150 8.2.3 Overdispersed Distributions 152 8.3 Monitoring Convergence 153 8.4 A Simulated Example 155 8.5 Data Application 158 8.6 Bibliographic Notes 162 Problems 162 9 Prediction 167 9.1 One-Step Ahead Predictors 168 9.1.1 Infinite Past 168 9.1.2 Finite Past 168 9.1.3 An Approximate Predictor 172 9.2 Multistep Ahead Predictors 173 9.2.1 Infinite Past 173 9.2.2 Finite Past 174 9.3 Heteroskedastic Models 175 9.3.1 Prediction of Volatility 176 9.4 Illustration 178 9.5 Rational Approximations 180 9.5.1 Illustration 182 9.6 Bibliographic Notes Problems 184 10 Regression 187 10.1 Linear Regression Model 188 10.1.1 Grenander conditions 188 10.2 Properties of the LSE 191 10.2.1 Consistency 192 10.2.2 Asymptotic Variance 193 10.2.3 Asymptotic Normality 193 10.3 Properties of the BLUE 194 10.3.1 Efficiency of the LSE Relative to the BLUE 195 10.4 Estimation of the Mean 198 10.4.1 Consistency 198 10.4.2 Asymptotic Variance 199 10.4.3 Normality 200 10.4.4 Relative Efficiency 200 10.5 Polynomial Trend 202 10.5.1 Consistency 203 10.5.2 Asymptotic Variance 203 10.5.3 Normality 204 10.5.4 Relative Efficiency 204 10.6 Harmonic Regression 205 10.6.1 Consistency 205 10.6.2 Asymptotic Variance 205 10.6.3 Normality 205 10.6.4 Efficiency 206 10.7 Illustration: Air Pollution Data 207 10.8 Bibliographic Notes 210 Problems 211 11 Missing Data 215 11.1 Motivation 216 11.2 Likelihood Function with Incomplete Data 217 11.2.1 Integration 217 11.2.2 Maximization 218 11.2.3 Calculation of the Likelihood Function 219 11.2.4 Kalman Filter with Missing Observations 219 11.3 Effects of Missing Values on ML Estimates 221 11.3.1 Monte Carlo Experiments 222 11.4 Effects of Missing Values on Prediction 223 11.5 Illustrations 227 11.6 Interpolation of Missing Data 229 11.6.1 Bayesian Imputation 234 11.6.2 A Simulated Example 235 11.7 Bibliographic Notes 239 Problems 239 12 Seasonality 245 12.1 A Long-Memory Seasonal Model 246 12.2 Calculation of the Asymptotic Variance 250 12.3 Autocovariance Function 252 12.4 Monte Carlo Studies 254 12.5 Illustration 258 12.6 Bibliographic Notes 260 Problems 261 References 265 Topic Index 279 Author Index 283

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Book SynopsisThis hands-on book presents a complete understanding of Six Sigma and Lean Six Sigma through data analysis and statistical concepts In today''s business world, Six Sigma, or Lean Six Sigma, is a crucial tool utilized by companies to improve customer satisfaction, increase profitability, and enhance productivity. Practitioner''s Guide to Statistics and Lean Six Sigma for Process Improvements provides a balanced approach to quantitative and qualitative statistics using Six Sigma and Lean Six Sigma methodologies. Emphasizing applications and the implementation of data analyses as they relate to this strategy for business management, this book introduces readers to the concepts and techniques for solving problems and improving managerial processes using Six Sigma and Lean Six Sigma. Written by knowledgeable professionals working in the field today, the book offers thorough coverage of the statistical topics related to effective Six Sigma and Lean Six Sigma practiceTrade Review"The book would be of use for those working in the fields of engineering, business, physics, management and finance who are already familiar with the concepts of lean six sigma." (QW, July 2010) Table of ContentsPreface. 1 Principles of Six Sigma. 1.1 Overview. 1.2 Six Sigma Essentials. 1.2.1 Driving Need. 1.2.2 Customer Focus. 1.2.3 Core Beliefs. 1.2.4 Deterministic Reasoning. 1.2.5 Leverage Principle. 1.3 Quality Definition. 1.4 Value Creation. 1.4.1 Value. 1.5 Business, Operations, Process and Individual (BOPI) Goals. 1.5.1 Differences between Product and Process Capability from a Six Sigma Perspective. 1.6 Underpinning Economics. 1.6.1 Sigma Benchmarking. 1.6.2 Breakthrough Goals. 1.6.3 Performance Benchmark. 1.7 Performance Metrics. 1.8 Process. 1.8.1 Process Models. 1.9 Design Complexity. 1.10 Nature and Purpose of Six Sigma. 1.10.1 Not Just Defect Reduction. 1.11 Needs That Underlie Six Sigma. 1.11.1 Looking Across the Organization. 1.11.2 Processing for Six Sigma. 1.11.3 Designing for Six Sigma. 1.11.4 Managing for Six Sigma. 1.11.5 Risk Orientation. 1.12 Why Focusing on The Customer is Essential to Six Sigma. 1.13 Success Factors. 1.14 Software Applications. Explore Excel. Explore MINITAB. Explore JMP. Glossary. References. 2 Six Sigma Installation. 2.1 Overview. 2.2 Six Sigma Leadership-The Fuel of Six Sigma. 2.3 Deployment Planning. 2.3.1 Executive Management. 2.3.2 Six Sigma Champion. 2.3.3 Line Management. 2.3.4 Master Black Belts. 2.3.5 Black Belts. 2.3.6 Green Belts. 2.3.7 White Belts. 2.3.8 Six Sigma Roadmap. 2.3.9 Characteristics of Effective Metrics. 2.3.10 The Role of Metrics. 2.3.11 Six Sigma Performance Metrics. 2.3.12 Profit and Measurement 2.3.13 Twelve Criteria for Performance Metrics. 2.4 Application Projects. 2.5 Deployment Timeline. 2.6 Design for Six Sigma [DFSS] Principles. 2.7 Processing for Six Sigma [PFSS] Principles. 2.8 Managing for Six Sigma [MPSS] Principles. 2.9 Project Review. 2.9.1 Tollgate Criteria. 2.9.2 Project Closure. 2.9.3 Project Documentation. 2.9.4 Personal Recognition. 2.9.5 Authenticating Agent. 2.10 Summary. Glossary. References and Notes. 3 Lean Sigma Projects. 3.1 Overview. 3.2 Introduction. 3.3 Project Description. 3.4 Project Guidelines. 3.5 Project Selection. 3.5.1 Project Selection Guidelines. 3.6 Project Scope. 3.7 Project Leadership. 3.8 Project Teams. 3.9 Project Financials. 3.10 Project Management. 3.11 Project Payback. 3.12 Project Milestones. 3.13 Project Roadmap. 3.14 Project Charters (General). 3.15 Six Sigma Projects. 3.16 Project Summary. Glossary. References. 4 Lean Practices. 4.1 Overview. 4.2 Introduction. 4.3 The Idea of Lean Thinking. 4.4 Theory of Constraints [TOC]. 4.5 Lean Concept. 4.6 Value-Added Versus Non-Value-Added Activities. 4.7 Why Companies Think Lean. 4.8 Visual Controls-Visual Factory. 4.9 The Idea of Pull (Kanban). 4.10 5S-6S Approach. 4.11 The Idea of Perfection (Kaizen). 4.12 Replication-Translate. 4.13 Poka-Yoke System-Mistakeproofing. 4.14 SMED System. 4.15 7W + 1 Approach-Seven Plus One Deadly Waste(s). 4.16 6M Approach. 4.17 Summary. Glossary. References. 5 Value Stream Mapping. 5.1 Overview. 5.2 Introduction. 5.3 Value Stream Mapping. 5.3.1 Waste Review. 5.3.2 Value-Added and Non-Value-Added Activities. 5.3.3 Elements of a Value Stream Map. 5.4 Focused Brainstorming. 5.5 Graphical representation of a Process in a Value Stream Map. 5.6 Effective Working Time. 5.7 Customer Demand. 5.8 Takt Time. 5.9 Pitch Time. 5.10 Queuing Time. 5.11 Cycle Time. 5.12 Total Cycle Time. 5.13 Calculation of Total Lead Time(s). 5.14 Value-Added Percentage and Six Sigma Level. 5.15 Drawing the Current-Value-Stream Map. 5.15.1 Drawing Tips. 5.15.2 Common Failure Modes. 5.15.3 Common Definitions. 5.16 Drawing the Value Stream Map. 5.17 What Makes a Value Stream Lean. 5.18 The Future Value Stream Map. 5.19 Summary. Glossary. References and Notes. 6 Introductory Statistics and Data. 6.1 Overview. 6.2 Introduction. 6.3 Genetic Code of Statistics. 6.4 Population and Samples. 6.5 The Idea of Data. 6.6 Nature of Data. 6.6.1 Quantitative Variables and Data. 6.6.2 Qualitative/Categorical Variables and Data. 6.7 Data Collection. 6.8 The Importance of Data Collection. 6.8.1 Control Cards. 6.8.2 Data Collection Sheet. 6.9 Sampling in Six Sigma. 6.9.1 Random Sampling. 6.9.2 Sequential Sampling. 6.9.3 Stratified Sampling. 6.10 Sources of Data. 6.11 Database. 6.12 Summary. Glossary. References. 7 Quality Tools. 7.1 Overview. 7.2 Introduction. 7.3 Nature of Six Sigma Variables. 7.3.1 CT Concept. 7.3.2 CTQ and CTP Characteristics. 7.3.3 CTX Tree (Process Tree). 7.3.4 CTY Tree (Process Tree). 7.3.5 The Focus of Six Sigma. 7.3.6 The Leverage Principle. 7.4 Quality Function Deployment (QFD). 7.5 Scales of Measurement. 7.5.1 Likert Scale. 7.5.2 Logarithm Scale. 7.6 Diagnostic Tools. 7.6.1 Elements for Problem Solving-Diagnostic Tools and Methods. 7.6.2 Problem Definition-Defining Project Objective. 7.7 Analytical Methods. 7.7.1 Cause-Effect (CE) Analysis. 7.7.2 Failure Mode-Effects Analysis (FMEA) 7.7.3 XY Matrix. 7.8 Graphical Tools. 7.8.1 Graphical Summary. 7.8.2 Boxplot or Box-and-Whisker Plot. 7.8.3 Normal Probability Plot. 7.8.4 Main-Effects Plot. 7.8.5 Pareto Chart. 7.8.6 Run Chart. 7.8.7 Time-Series Plot. 7.8.8 Multi-Vari Charts. 7.8.9 Scatterplot. 7.9 Graphical Representation of a Process. 7.9.1 Process Flowcharts. 7.9.2 Process Mapping. 7.9.3 Cross-Functional Mapping. 7.9.4 Process Mapping-Deployment Diagram. 7.10 SIPOC Diagram. 7.11 IPO Diagram-General Model of a Process System. 7.12 Force-Field Analysis. 7.13 Matrix Analysis-The Importance of Statistical Thinking. 7.14 Checksheets. 7.15 Scorecards. 7.16 Affinity Diagram. 7.17 Concept Integration. Glossary. Reference. 8 Making Sense of Data in Six Sigma and Lean. 8.1 Overview. 8.2 Summarizing Quantitative Data: Graphical Methods. 8.2.1 Analytical Charts. 8.2.2 Dotplots. 8.2.3 Stem-and-Leaf Plots. 8.2.4 Frequency Tables. 8.2.5 Histograms and Performance Histograms. 8.2.6 Run Charts. 8.2.7 Time-Series Plots. 8.3 Summarizing Quantitative Data: Numerical Methods. 8.3.1 Measures of Center. 8.3.2 Measures of Variation. 8.3.3 Identifying Potential Outliers. 8.3.4 Measures of Position and the Idea of z Scores in Six Sigma. 8.3.5 Measure of Spread and Lean Sigma. 8.4 Organizing and Graphing Qualitative Data. 8.4.1 Organizing Qualitative Data. 8.4.2 Graphing Qualitative Data. 8.4.3 Pareto Analysis with Lorenz Curve. 8.5 Summarizing Bivariate Data. 8.5.1 Scatterplot. 8.5.2 Correlation Coefficient. 8.6 Multi-Vari Charts. Glossary. Exercises. 9 Fundamentals of Capability and Rolled Throughput Yield. 9.1 Overview. 9.2 Introduction. 9.3 Why Capability. 9.3.1 Performance Specifications. 9.3.2 Fundamental Concepts of Defect-Based Measurement. 9.4 Six Sigma Capability Metric. 9.4.1 Criteria for Performance Metrics. 9.4.2 Computing the Sigma Level from Discrete Data. 9.4.3 Defective Proportions. 9.4.4 Six-Sigma-Level Calculations (DPU, DPO, DPMO, PPM)-Examples. 9.5 Discrete Capability. 9.6 Continuous Capability-Example. 9.6.1 Data Collection for Capability Studies. 9.7 Fundamentals of Capability. 9.8 Short- Versus Long-Term Capability. 9.8.1 Short-Term Capability. 9.8.2 Long-Term Capability 9.8.3 Introduction to Calibrating the Shift. 9.9 Capability and Performance. 9.10 Indices of Capability. 9.10.1 Cp Index. 9.10.2 Cpk Index. 9.10.3 Pp Index. 9.10.4 Ppk Index. 9.11 Calibrating the Shift. 9.12 Applying the 1.5σ Shift Concept. 9.13 Yield. 9.13.1 Final Test Yield (FTY). 9.13.2 Yield Related to Defects. 9.13.3 Rolled Throughput Yield (RTY). 9.13.4 In-Process Yield (IPY). 9.13.5 In-Process Yield (IPY) and Rolled Throughput Yield (RTY). 9.14 Hidden Factory. 9.14.1 Hidden Factory Composition. Glossary. References. 10 Probability. 10.1 Overview. 10.2 Experiments, Outcomes, and Sample Space. 10.3 Calculating Probability. 10.3.1 Equally Likely Events. 10.3.2 Probability as Relative Frequency. 10.3.3 Subjective Probability. 10.4 Combinatorial Probability. 10.5 Marginal and Conditional Probabilities. 10.6 Union of Events. 10.6.1 Addition Role. 10.6.2 Mutually Exclusive Events. 10.6.3 Complementary Events. 10.7 Intersection of Events. 10.7.1 Independent Versus Dependent Events. 10.7.2 Multiplication Rule. Glossary. Exercises. 11 Discrete Random Variables and Their Probability Distributions. 11.1 Overview. 11.2 Six Sigma Performance Variables. 11.3 Six Sigma Leverage Variables. 11.4 Random Variables. 11.4.1 Discrete Random Variables. 11.4.2 Continuous Random Variables. 11.5 Probability Distributions of a Discrete Random Variable. 11.6 Mean of a Random Variable. 11.7 Standard Deviation of a Discrete Random Variable. 11.8 The Binomial Distribution. 11.8.1 Factorials and Combinations. 11.8.2 The Binomial Experiment. 11.8.3 The Binomial Probability Distribution and Binomial Formula. 11.8.4 Probability of Success and Shape of the Binomial Distribution. 11.8.5 Mean and Standard Deviation of the Binomial Distribution. 11.9 The Poisson Probability Distribution. 11.9.1 Mean and Standard Deviation of the Poisson Probability Distribution. 11.10 The Geometric Distribution. 11.11 The Hypergeometric Probability Distribution. Glossary. Exercises. 12 Continuous Random Variables and Their Distributions. 12.1 Overview. 12.2 Continuous Probability Distributions. 12.3 The Normal Distribution. 12.3.1 The Empirical Rule. 12.3.2 The Standard Normal Distribution. 12.3.3 Applications of the Normal Distribution. 12.4 The Exponential Distribution. Glossary. Exercises. 13 Sampling Distributions. 13.1 Overview. 13.2 Sampling Distribution of a Sample Mean. 13.2.1 Sampling and Nonsampling Errors. 13.3 Sampling Distribution of a Sample Proportion. 13.4 The Central-Limit Theorem (CLT). 13.4.1 The CLT and Sampling Distribution of the Sample Mean. 13.4.2 The CLT and Sampling Distribution of the Sample Proportion. Glossary. Exercises. 14 Single-Population Estimation. 14.1 Overview. 14.2 Meaning of a Confidence Level. 14.3 Estimating a Population Mean. 14.3.1 Confidence Interval for a Population Mean Using the Normal Distribution. 14.3.2 Confidence Interval for a Population Mean Using the t Distribution. 14.4 Estimating a Population Proportion. 14.4.1 Traditional Large-Sample Method. 14.4.2 Wilson Estimator. 14.5 Estimating a Population Variance. Glossary. Exercises. 15 Control Methods. 15.1 Overview. 15.2 Introduction. 15.3 Control Logic. 15.4 Statistical Control Systems. 15.4.1 15.5 Statistical Control. 15.6 Prevention Versus Detection. 15.7 A Process Control System Definition. 15.8 Variation. 15.8.1 Common Causes. 15.8.2 Special Causes. 15.9 Process Out-of-Control. 15.10 Fundamentals of Process Control. 15.11 Continuous Statistical Process Control (SPC) Tools. 15.12 Interpreting Process Control. 15.13 Statistical Process Control and Statistical Process Monitoring. 15.14 The Foundation of SPC. 15.15 Tools for Process Controls - Control Charts. 15.16 Control Limits. 15.17 Process Out-of-Control Condition. 15.18 Western Electric Rules. 15.19 Control Charts and How They Are Used. 15.20 Precontrol Method. 15.20.1 The Foundations of Precontrol. 15.20.2 Precontrol Charts. 15.21 Control Charts for Variables. 15.21.1 X Chart. 15.21.2 R Chart (Range Chart). 15.21.3 X-R Chart. 15.21.4 Moving Range (MR) Chart. 15.21.5 Standard Deviation Chart. 15.22 Control Chart for Attributes. 15.22.1 p Chart. 15.22.2 Control Chart-np Chart. 15.22.3 c Chart. 15.22.4 u Chart. Glossary. References and Notes. 16 Single-Population Hypothesis Tests. 16.1 Overview. 16.2 Introduction to Hypothesis Testing. 16.3 Testing a Claim About a Population Mean. 16.3.1 Hypothesis Test Using the Normal Distribution. 16.3.2 Hypothesis Test Using the t Distribution. 16.3.3 Hypothesis Test About the Median. 16.4 Hypothesis Test About a Population Proportion. Glossary. Exercises. 17 Estimation and Hypothesis Tests: Two Populations. 17.1 Overview. 17.2 Inferences About the Differences Between Two Population Means for Independent Samples. 17.2.1 Two-Sample t Test. 17.2.2 Mann-Whitney Test 17.3 Inferences About the Differences Between Two Population Means for Paired Samples. 17.3.1 Paired t Test. 17.3.2 Wilcoxon Signed-Rank Test. 17.4 Inferences About the Differences Between Two Population Proportions. 17.4.1 Large-Sample Procedure. Glossary. Exercises. 18 Chi-Square Tests. 18.1 Overview. 18.2 A Goodness-of-Fit Test. 18.3 Contingency Tables. 18.4 Tests of Independence and Homogeneity. 18.4.1 Test of Independence. 18.4.2 Test of Homogeneity. Glossary. Exercises. 19 Analysis of Variance. 19.1 Overview. 19.2 The F Distribution. 19.3 One-Way Analysis of Variance. 19.3.1 Variance Between Groups. 19.3.2 Variance Within Groups. 19.3.3 Total Sum of Squares (SST). 19.3.4 Relationships within Sums of Squares and Degrees of Freedom. 19.3.5 Equal Sample Sizes. 19.3.6 Calculating the Value of the Test Statistic. 19.3.7 The One-Way ANOVA Table. 19.4 Pairwise Comparisons. 19.5 Multi-Factor Analysis of Variance. 19.5.1 Two-Way ANOVA 19.5.2 N-Way ANOVA. 19.6 What to Do When the Assumptions Are Unreasonable. Glossary. Exercises. 20 Linear and Multiple Regression. 20.1 Overview. 20.2 Simple Regression Model. 20.3 Linear Regression. 20.3.1 Simple Linear Regression. 20.3.2 Scatterplots. 20.3.3 Assumptions of the Regression Model. 20.3.4 Standard Deviation of Random Errors. 20.4 Coefficient of Determination and Correlation. 20.5 Multiple Regression. 20.5.1 Assumptions of the Multiple Regression Model. 20.5.2 Standard Deviation of Random Errors. 20.5.3 Coefficient of Multiple Determination. 20.6 Regression Analysis. 20.6.1 Testing for Overall Significance of Multiple Regression Model. 20.6.2 Inferences about a Single Regression Coefficient, Bi. 20.7 Using the Regression Model. 20.8 Residual Analysis. 20.9 Cautions in Using Regression. 20.9.1 Determining whether a Model is Good or Bad. 20.9.2 Outliers and Influential Observations. 20.9.3 Multicollinearity. 20.9.4 Extrapolation. 20.9.5 Causality. Glossary. Exercises. 21 Measurement Analysis. 21.1 Overview. 21.2 Introduction. 21.3 Measurement. 21.4 Measurement Error. 21.5 Accuracy and Precision. 21.6 Measurement System as a Process. 21.7 Categories of Measurement Error that Affect Location. 21.8 Categories of Measurement that Affect Spread. 21.9 Gage Accuracy and Precision. 21.10 Exploring Linearity Error. 21.11 Gage Repeatability and Reproducibility (R&R). 21.11.1 Variable Gage R&R. 21.11.2 Crossed Gage R&R. 21.11.3 Attribute Gage R&R. 21.12 ANOVA Method Versus X-R Method. 21.13 ANOVA/Variance Component Analysis. 21.14 Rules of Thumb. 21.15 Acceptability Criteria. 21.16 Chapter Review. Glossary. References. 22 Design of Experiments. 22.1 Overview. 22.2 Introduction. 22.3 Design of Experiments (DOE)Definition. 22.4 Role of Experimental Design in Process Improvement. 22.5 Experiment Design Tools. 22.6 Principles of an Experimental Design. 22.7 Different Types of Experiments. 22.7.1 Main Effects. 22.8 Introduction to Factorial Designs. 22.9 Features of Factorial Designs-Orthogonality. 22.10 Full Factorial Designs. 22.11 Residual Analysis (22). 22.12 Modeling (22). 22.13 Multi-Factor Experiment. 22.14 Fractional Factorial Designs. 22.15 The ANOVA Table. 22.16 Normal Probability Plot of the Effects. 22.17 Main-Effects Plot. 22.18 Blocking Variable. 22.19 Statistical Significance. 22.20 Practical Significance. 22.21 Fundamentals of Residual Analysis. 22.22 Centerpoints. 22.23 Noise Factors. 22.24 Strategy of Good Experimentation. 22.25 Selecting the Variable Levels. 22.26 Selecting the Experimental Design. 22.27 Replication. 22.28 Analyzing the data (ANOVA). 22.29 Recommendations. 22.30 Achieving the Objective. 22.31 Chapter Summary. 22.32 Chapter Examples. Glossary. References. 23 Design for Six Sigma (DFSS), Simulation, and Optimization. 23.1 Overview. 23.2 Introduction. 23.3 Six Sigma as Stretch Target. 23.4 Producibility. 23.5 Statistical Tolerances. 23.6 Design Application. 23.7 Design Margin. 23.8 Design Qualification. 23.9 Design for Six Sigma (DFSS) Principles. 23.9.1 DFSS Leverage in Product Design. 23.9.2 Importance of DFSS for Product Design. 23.10 Decision Power. 23.11 Experimentation. 23.12 Experiment Design. 23.13 Response Surface Designs. 23.14 Factorial Producibility. 23.15 Toolbox Overview. 23.16 Monte Carlo Simulations. 23.16.1 Monte Carlo Simulation Defined. 23.16.2 When Simulation is an Appropriate Tool. 23.16.3 Defining Distributions and Outputs in Crystal Ball. 23.17 Design for Six Sigma Project Selection Example. 23.18 Defining Simulation Inputs. 23.19 Defining Outputs and Running a Simulation. 23.19.1 Analyzing a Simulation. 23.20 Stochastic Optimization: Discovering the Best Portfolio with the Least Risk. 23.21 Conclusions. Glossary. References. 24 Survey Methods and Sampling Techniques. 24.1 Overview. 24.2 Introduction. 24.3 The Sample Survey. 24.4 The Survey System. 24.5 Clear Goals. 24.6 Target Population and Sample Size. 24.7 Interviewing Method. 24.8 Response Rate, Respondents and Nonrespondents. 24.9 Survey Methods. 24.10 Sources of Information and Data. 24.11 Order of the Questions. 24.12 Pilot Testing the Questionnaire. 24.13 Biased Sample or Response Error. 24.14 Sampling-Random and Nonrandom Samples. 24.15 Population Distribution. 24.16 Sampling Distribution. 24.17 Sampling and Nonsampling Errors. Glossary. References. Appendix A Statistical Tables. Table I Table of Binomial Probabilities. Table II Standard Normal Distribution Table. Table III The t Distribution Table. Table IV Chi-Square Distribution Table. Table V The F Distribution Table. Table VI Critical Values for the Mann-Whitney Test. Table VII Critical Values for the Wilcoxon Signed-Rank Test. Table VIII Sigma Conversion Table. Appendix B Answers to Selected Odd-Numbered Exercises. Index.

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Book SynopsisMachine learning techniques such as Markov models, support vector machines, neural networks, graphical models, etc. , have been successful in analyzing life science data because of their capabilities of handling randomness and uncertainties of data and noise and in generalization.Table of ContentsForeword. Preface. Contributors. 1 Feature Selection for Genomic and Proteomic Data Mining (Sun-Yuan Kung and Man-Wai Mak). 2 Comparing and Visualizing Gene Selection and Classification Methods for Microarray Data (Rajiv S. Menjoge and Roy E. Welsch). 3 Adaptive Kernel Classifiers Via Matrix Decomposition Updating for Biological Data Analysis (Hyunsoo Kim and Haesun Park). 4 Bootstrapping Consistency Method for Optimal Gene Selection from Microarray Gene Expression Data for Classification Problems (Shaoning Pang, Ilkka Havukkala, Yingjie Hu, and Nikola Kasabov). 5 Fuzzy Gene Mining: A Fuzzy-Based Framework for Cancer Microarray Data Analysis (Zhenyu Wang and Vasile Palade). 6 Feature Selection for Ensemble Learning and Its Application (Guo-Zheng Li and Jack Y. Yang). 7 Sequence-Based Prediction of Residue-Level Properties in Proteins (Shandar Ahmad, Yemlembam Hemjit Singh, Marcos J. Araúzo-Bravo, and Akinori Sarai). 8 Consensus Approaches to Protein Structure Prediction (Dongbo Bu, ShuaiCheng Li, Xin Gao, Libo Yu, Jinbo Xu, and Ming Li). 9 Kernel Methods in Protein Structure Prediction (Jayavardhana Gubbi, Alistair Shilton, and Marimuthu Palaniswami). 10 Evolutionary Granular Kernel Trees for Protein Subcellular Location Prediction (Bo Jin and Yan-Qing Zhang). 11 Probabilistic Models for Long-Range Features in Biosequences (Li Liao). 12 Neighborhood Profile Search for Motif Refinement (Chandan K. Reddy, Yao-Chung Weng, and Hsiao-Dong Chiang). 13 Markov/Neural Model for Eukaryotic Promoter Recognition (Jagath C. Rajapakse and Sy Loi Ho). 14 Eukaryotic Promoter Detection Based on Word and Sequence Feature Selection and Combination (Xudong Xie, Shuanhu Wu, and Hong Yan). 15 Feature Characterization and Testing of Bidirectional Promoters in the Human Genome—Significance and Applications in Human Genome Research (Mary Q. Yang, David C. King, and Laura L. Elnitski). 16 Supervised Learning Methods for MicroRNA Studies (Byoung-Tak Zhang and Jin-Wu Nam). 17 Machine Learning for Computational Haplotype Analysis (Phil H. Lee and Hagit Shatkay). 18 Machine Learning Applications in SNP–Disease Association Study (Pritam Chanda, Aidong Zhang, and Murali Ramanathan). 19 Nanopore Cheminformatics-Based Studies of Individual Molecular Interactions (Stephen Winters-Hilt). 20 An Information Fusion Framework for Biomedical Informatics (Srivatsava R. Ganta, Anand Narasimhamurthy, Jyotsna Kasturi, and Raj Acharya). Index.

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Book SynopsisThis groundbreaking book introduces the application of statistical methodologies to e-Commerce data With the expanding presence of technology in today''s economic market, the use of the Internet for buying, selling, and investing is growing more popular and public in nature. Statistical Methods in e-Commerce Research is the first book of its kind to focus on the statistical models and methods that are essential in order to analyze information from electronic-commerce (e-Commerce) transactions, identify the challenges that arise with new e-Commerce data structures, and discover new knowledge about consumer activity. This collection gathers over thirty researchers and practitioners from the fields of statistics, computer science, information systems, and marketing to discuss the growing use of statistical methods in e-Commerce research. From privacy protection to economic impact, the book first identifies the many obstacles that are encountered while collecting,Table of ContentsPreface. Acknowledgements. Contributor List. Section I: Overview of E-Commerce Research Challenges. 1. Statistical Challenges in Internet Advertising (Deepak Agarwal). 2. How Has E-Commerce Research Advanced Understanding of the Offline World (Chris Forman and Avi Goldfarb)? 3. The Economic Impact of User-Generated and Firm-Generated Online Content: Directions for Advancing the Frontiers in Electronic Commerce Research (Anindya Ghose). 4. Is Privacy Protection for Data in an E-Commerce World an Oxymoron (Stephen E. Fienberg)? 5. Network Analysis of Wikipedia (Robert H. Warren, Edoardo M. Airoldi, and David L. Banks). Section II: E-Commerce Applications. 6. An Analysis of Price Dynamics, Bidder Networks, and Market Structure in Online Art Auctions (Mayukh Dass and Srinivas K. Reddy). 7. Modeling Web Usability Diagnostics on the Basis of Usage Statistics (Avi Harel, Ron S. Kenett, and Fabrizio Ruggeri). 8. Developing Rich Insights on Public Internet Firm Entry and Exit Based on Survival Analysis and Data Visualization (Robert J. Kauffman and Bin Wang). 9. Modeling Time-Varying Coefficients in Pooled Cross-Sectional E-Commerce Data: An Introduction (Eric Overby and Benn Konsynski). 10. Optimization of Search Engine Marketing Bidding Strategies Using Statistical Techniques (Alon Matas and Yoni Schamroth). Section III: New Methods For E-Commerce Data. 11. Clustering Data with Measurement Errors (Mahesh Kumar and Nitin R. Patel). 12. Functional Data Analysis for Sparse Auction Data (Bitao Liu and Hans-Georg Müller). 13. A Family of Growth Models for Representing the Price Process in Online Auctions (Valerie Hyde, Galit Shmueli, and Wolfgang Jank). 14. Models of Bidder Activity Consistent with Self-Similar Bid Arrivals (Ralph P. Russo, Galit Shmueli, and Nariankadu D. Shyamalkumar). 15. Dynamic Spatial Models for Online Markets (Wolfgang Jank and P.K. Kannan). 16. Differential Equation Trees to Model Price Dynamics in Online Auctions (Wolfgang Jank, Galit Shmueli, and Shanshan Wang). 17. Quantile Modeling for Wallet Estimation (Claudia Perlich and Saharon Rosset). 18. Applications of Randomized Response Methodology in E-Commerce (Peter G.M. van der Heijden and Ulf Böckenholt). Index.

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Book SynopsisProviding a bridge between a problem and its solution through visualization, this book covers the most talked about problems currently available. Presenting a new approach that allows the reader to work by designing C++ programs directly using Windows interface in one book, the text provides ready to run codes.Trade Review"The clarity of the book is excellent." (CHOICE, May 2008)Table of ContentsChapter 1: Overview of C++. Language style and organization. Data types, variables. Loops and branches. Array, pointer, function, structure. Classes and objects. Inheritance, polymorphism, encapsulation. Complexity analysis. Chapter 2: Visual C++ Methods. MFC library . Fundamental interface tools. Text displays. Graphics and images. Writing the first program. Chapter 3: Fundamental Mathematical Tools. C++ for High-Performance Computing. Dynamic memory allocation. Allocation for one-dimensional arrays. Allocation for higher-dimensional arrays. Case Study: Matrix multiplication problem. Matrix elimination problems. Vector and matrix norms. Row operations. Matrix reduction to triangular form. Computing the determinant of a matrix. Computing the inverse of a matrix. Matrix algebra. Data passing between functions. Matrix addition and subtraction. Matrix multiplication. Matrix inverse. Putting the pieces together. Algebra of complex numbers. Addition and subtraction. Multiplication. Conjugate. Division. Inverse of a complex number. Putting the pieces together. Number Sorting. Programming Exercises. Chapter 4: System of Linear Equations. Systems of Linear Systems. Existence of Solutions. Elimination Techniques. Gauss Elimination Method. Gauss Elimination with Partial Pivoting. Gauss-Jordan Method. LU Factorization Techniques. Crout Method. Doolittle Method. Cholesky Method. Thomas Algorithm. Iterative Techniques. Jacobi Method. Gauss-Seidel Method. Visual C++ Solution Interface. Summary. Programming Exercises. Chapter 5: Nonlinear Equations. Iterative methods: convergence, stability. Background: existence of solution, MVT, errors, etc.. Bisection method. False-point position method. Newton method. Secant method. Fixed-point iterative method. Visual C++ Solution Interface. Summary. Programming Exercises. Chapter 6: Interpolation and Approximation. Concepts, existence, stability. Lagrange. Newton methods: forward, backward. Stirling method. Cubic spline interpolation. Least-square approximation. Visual C++ Solution Interface. Summary. Programming Exercises. Chapter 7: Differentiation and Integration. Taylor series. Newton methods (forward, backward, central). Trapezium method. Simpson method. Simpson 3/8 method. Gauss quadrature. Visual C++ Solution Interface. Summary. Programming Exercises. Chapter 8: Eigenvalues and Eigenvectors. Characteristic polynomials. Power method. Power method with shifting. Visual C++ Solution Interface. Summary. Programming Exercises. Chapter 9: Ordinary Differential Equations. Existence, uniqueness, stability, convergence. IVP: Taylor method. Euler method. Runge-Kutta of order 2 method. Runge-Kutta of order 4 method. Higher dimensional orders. Multistep methods: Adams-Bashforth method. Boundary Value Problems: finite-difference method. Visual C++ Solution Interface. Summary. Programming Exercises. Chapter 10: Partial Differential Equations. Existence, uniqueness, stability, convergence. Elliptic problem: Laplace equation. Elliptic problem: Poisson equation. Parabolic problem: heat equation. Hyperbolic problem: wave equation. Visual C++ Solution Interface. Summary. Programming Exercises. Chapter 11: Finite Element Methods. One-dimensional heat problem. Linear approximation. Quadratic approximation. Two-dimensional problem: triangulation method. Visual C++ Solution Interface. Summary. Programming Exercises.

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Book SynopsisA new edition of the classic, groundbreaking book on robust statistics Over twenty-five years after the publication of its predecessor, Robust Statistics, Second Edition continues to provide an authoritative and systematic treatment of the topic. This new edition has been thoroughly updated and expanded to reflect the latest advances in the field while also outlining the established theory and applications for building a solid foundation in robust statistics for both the theoretical and the applied statistician. A comprehensive introduction and discussion on the formal mathematical background behind qualitative and quantitative robustness is provided, and subsequent chapters delve into basic types of scale estimates, asymptotic minimax theory, regression, robust covariance, and robust design. In addition to an extended treatment of robust regression, the Second Edition features four new chapters covering: Robust Tests Small SaTrade Review"A comprehensive introduction and discussion on the formal mathematical background behind qualitative and quantitative robustness is provided, and subsequent chapters delve into basic types of scale estimates, asymptotic minimax theory, regression, robust covariance, and robust design . . . it also serves as a valuable reference for researchers and practitioners who wish to study the statistical research associated with robust statistics" (Mathematical Reviews, 2010)Table of ContentsPreface. Preface to First Edition. 1. Generalities. 1.1 Why Robust Procedures? 1.2 What Should a Robust Procedure Achieve? 1.3 Qualitative Robustness. 1.4 Quantitative Robustness. 1.5 Infinitesimal Aspects. 1.6 Optimal Robustness. 1.7 Computation of Robust Estimates. 1.8 Limitations to Robustness Theory. 2. The Weak Topology and its Metrization. 2.1 General Remarks. 2.2 The Weak Topology. 2.3 Lévy and Prohorov Metrics. 2.4 The Bounded Lipschitz Metric. 2.5 Fréechet and Gâteaux Derivatives. 2.6 Hampel’s Theorem. 3. The Basic Types of Estimates. 3.1 General Remarks. 3.2 Maximum Likelihood Type Estimates (MEstimates). 3.3 Linear Combinations of Order Statistics (LEstimates). 3.4 Estimates Derived from Rank Tests (REstimates). 3.5 Asymptotically Efficient M, L, and REstimates. 4. Asymptotic Minimax Theory for Estimating Location. 4.1 General Remarks. 4.2 Minimax Bias. 4.3 Minimax Variance: Preliminaries. 4.4 Distributions Minimizing Fisher Information. 4.5 Determination of F0 by Variational Methods. 4.6 Asymptotically Minimax MEstimates. 4.7 On the Minimax Property for Land REstimates. 4.8 Redescending MEstimates. 4.9 Questions of Asymmetric Contamination. 5. Scale Estimates. 5.1 General Remarks. 5.2 MEstimates of Scale. 5.3 LEstimates of Scale. 5.4 REstimates of Scale. 5.5 Asymptotically Efficient Scale Estimates. 5.6 Distributions Minimizing Fisher Information for Scale. 5.7 Minimax Properties. 6. Multiparameter Problems, in Particular Joint Estimation of Location and Scale. 6.1 General Remarks. 6.2 Consistency of MEstimates. 6.3 Asymptotic Normality of MEstimates. 6.4 Simultaneous MEstimates of Location and Scale. 6.5 MEstimates with Preliminary Estimates of Scale. 6.6 Quantitative Robustness of Joint Estimates of Location and Scale. 6.7 The Computation of MEstimates of Scale. 6.8 Studentizing. 7. Regression. 7.1 General Remarks. 7.2 The Classical Linear Least Squares Case. 7.2.1 Residuals and Outliers. 7.3 Robustizing the Least Squares Approach. 7.4 Asymptotics of Robust Regression Estimates. 7.5 Conjectures and Empirical Results. 7.6 Asymptotic Covariances and Their Estimation. 7.7 Concomitant Scale Estimates. 7.8 Computation of Regression MEstimates. 7.9 The Fixed Carrier Case: what size hi? 7.10 Analysis of Variance. 7.11 L1estimates and Median Polish. 7.12 Other Approaches to Robust Regression. 8. Robust Covariance and Correlation Matrices. 8.1 General Remarks. 8.2 Estimation of Matrix Elements Through Robust Variances. 8.3 Estimation of Matrix Elements Through Robust Correlation. 8.4 An Affinely Equivariant Approach. 8.5 Estimates Determined by Implicit Equations. 8.6 Existence and Uniqueness of Solutions. 8.7 Influence Functions and Qualitative Robustness. 8.8 Consistency and Asymptotic Normality. 8.9 Breakdown Point. 8.10 Least Informative Distributions. 8.11 Some Notes on Computation. 9. Robustness of Design. 9.1 General Remarks. 9.2 Minimax Global Fit. 9.3 Minimax Slope. 10. Exact Finite Sample Results. 10.1 General Remarks. 10.2 Lower and Upper Probabilities and Capacities. 10.3 Robust Tests. 10.4 Sequential Tests. 10.5 The NeymanPearson Lemma for 2Alternating Capacities. 10.6 Estimates Derived From Tests. 10.7 Minimax Interval Estimates. 11. Finite Sample Breakdown Point. 11.1 General Remarks. 11.2 Definition and Examples. 11.3 Infinitesimal Robustness and Breakdown. 11.4 Malicious versus Stochastic Breakdown. 12. Infinitesimal Robustness. 12.1 General Remarks. 12.2 Hampel’s Infinitesimal Approach. 12.3 Shrinking Neighborhoods. 13. Robust Tests. 13.1 General Remarks. 13.2 Local Stability of a Test. 13.3 Tests for General Parametric Models in the Multivariate Case. 13.4 Robust Tests for Regression and Generalized Linear Models. 14. Small Sample Asymptotics. 14.1 General Remarks. 14.2 Saddlepoint Approximation for the Mean. 14.3 Saddlepoint Approximation of the Density of Mestimators. 14.4 Tail Probabilities. 14.5 Marginal Distributions. 14.6 Saddlepoint Test. 14.7 Relationship with Nonparametric Techniques. 15. Bayesian Robustness. 15.1 General Remarks. 15.2 Disparate Data and Problems with the Prior. 15.3 Maximum Likelihood and Bayes Estimates. 15.4 Some Asymptotic Theory. 15.5 Minimax Asymptotic Robustness Aspects. 15.6 Nuisance Parameters. 15.7 Why there is no Finite Sample Bayesian Robustness Theory. References. Index.

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Book SynopsisGet this comprehensive guide to the use of math in the Green Industry. Designed for both students and practitioners in the Green Industry, this book offers full coverage of the calculations necessary to effectively, safely, and economically manage a Green Industry operation. The authors provide clear explanations of all relevant mathematical principles and cover calculations inherent in all aspects of the Green Industry, from determining area and volume, to the application of fertilizers, pesticides, and growth regulators, to preparing design and installation cost estimates. Coverage includes computations for: Landscape installation and maintenance. Greenhouse, nursery, and interior landscape operation. Parks and recreation maintenance. Turf management, including lawn care, sports turf, and sod production. Proper application of fertilizers, pesticides, and plant-growth regulators. Proper calibration of application equipment.Table of ContentsPreface v About the Authors vii Part 1 Mathematical Principles 1 Chapter 1 Basic Math Skills 1 Chapter 2 Measurement and Calculations with Measured Values 31 Chapter 3 Geometry 47 Part 2 Green Industry Applications 79 Chapter 4 Calculating the Area of Landscape Features 79 Chapter 5 Fertilizer Calculations 95 Chapter 6 Pesticide and Plant Growth Regulator Calculations 127 Chapter 7 Calibration of Application Equipment 151 Chapter 8 Mathematical Applications for the Turfgrass Industry 169 Chapter 9 Mathematical Applications for the Landscape Industry 191 Chapter 10 Mathematical Applications for the Greenhouse, Nursery, and Interior Landscape Industries 223 Appendix A: Metric System Prefixes 285 Appendix B: Tables of Equivalents 287 Appendix C: Table of Conversion Factors 297 Appendix D: Squaring-Up Gardens and Garden Structures 307 Appendix E: Solutions to Practice Problems 309 Index 395

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Book SynopsisA balanced presentation of the theoretical, practical, and computational aspects of nonlinear regression. This book provides background material on linear regression, including a geometrical development for linear and nonlinear least squares.Table of ContentsReview of Linear Regression. Nonlinear Regression: Iterative Estimation and Linear Approximations. Practical Considerations in Nonlinear Regression. Multiresponse Parameter Estimation. Models Defined by Systems of Differential Equations. Graphical Summaries of Nonlinear Inference Regions. Curvature Measures of Nonlinearity. Appendix 1: Data Sets Used in Examples. Appendix 2: QR Decompositions Using Householder Transformations. Appendix 3: Pseudocode for Computing Algorithms. Appendix 4: Data Sets Used in Problems. Appendix 5: Evaluating Matrix Exponentials and Convolutions. Appendix 6: Interpolating Profile Pair Contours. Appendix 7: Key to Data Sets. References. Bibliography. Index.

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Book SynopsisPraise for the First Edition: For a beginner [this book] is a treasure trove; for an experienced person it can provide new ideas on how better to pursue the subject of applied statistics. Journal of Quality Technology Sensibly organized for quick reference, Statistical Rules of Thumb, Second Edition compiles simple rules that are widely applicable, robust, and elegant, and each captures key statistical concepts. This unique guide to the use of statistics for designing, conducting, and analyzing research studies illustrates real-world statistical applications through examples from fields such as public health and environmental studies. Along with an insightful discussion of the reasoning behind every technique, this easy-to-use handbook also conveys the various possibilities statisticians must think of when designing and conducting a study or analyzing its data. Each chapter presents clearly defined rules related to inference, covariatTrade Review?This is a unique and effective contribution. Unlike some statistical books, this is a truly enjoyable read.? (Doody?s Reviews) "For the applied researcher who does much of her or his own data analysis, this book is a must-have. Even the applied statistician would benefit from owning a copy of this collection. It is certain that some 'rules' will be new, and the descriptions in the text can come in quite handy when one i trying to explain a concept to a non-statistician. In short, this collection of 'rules' is highly recommended." (MAA Reviews, December 10, 2008) "For the applied researcher who does much of her or his own data analysis, this book is a must-have. Even the applied statistician would benefit from owning a copy of this collection. It is certain that some 'rules' will be new, and the descriptions in the text can come in quite handy when one is trying to explain a concept to a non-statistician. In short, this collection of 'rules' is highly recommended." (MAA Reviews, Dec 2008)Table of ContentsPreface to the Second Edition. Preface to the First Edition. Acronyms. 1. The Basics. 1.1 Four Basic Questions. 1.2 Observation is Selection. 1.3 Replicate to Characterize Variability. 1.4 Variability Occurs at Multiple Levels. 1.5 Invalid Selection is the Primary Threat to Valid Inference. 1.6 There is Variation in Strength of Inference. 1.7 Distinguish Randomized and Observational Studies. 1.8 Beware of Linear Models. 1.9 Keep Models As Simple As Possible, But Not More Simple. 1.10 Understand Omnibus Quantities. 1.11 Do Not Multiply Probabilities More Than Necessary. 1.12 Use Two-sided p-Values. 1.13 p-Values for Sample Size, Confidence Intervals for Results. 1.14 At Least Twelve Observations for a Confidence Interval. 1.15 Estimate ± Two Standard Errors is Remarkably Robust. 1.16 Know the Unit of the Variable. 1.17 Be Flexible About Scale of Measurement Determining Analysis. 1.18 Be Eclectic and Ecumenical in Influence. 2. Sample Size. 2.1 Begin with a Basic Formula for Sample Size-Lehr’s Equation. 2.2 Calculating Sample Size Using the Coefficient of Variation. 2.3 No Finite Population Correction for Survey Sample Size. 2.4 Standard Deviation and Sample Range. 2.5 Do Not Formulate a Study Solely in Terms of Effect Size. 2.6 Overlapping Confidence Intervals Do Not Imply Nonsignificance. 2.7 Sample Size Calculation for the Poisson Distribution. 2.8 Sample Size for Poisson with Background Rate. 2.9 Sample Size Calculation for the Binomial Distribution. 2.10 When Unequal Sample Sizes Matters; When They Don’t. 2.11 Sample Size With Different Costs for the Two Samples. 2.12 The Rule of Threes for 95% Upper Bounds When There Are No Events. 2.13 Sample Size Calculations Are Determined by the Analysis. 3. Observational Studies. 3.1 The Model for an Observational Study is the Sample Survey. 3.2 Large Sample Size Does Not Guarantee Validity. 3.3 Good Observational Studies Are Designed. 3.4 To Establish Cause Effect Requires Longitudinal Data. 3.5 Make Theories Elaborate to Establish Cause and Effect. 3.6 The Hill Guidelines Are a Useful Guide to Show Cause Effect. 3.7 Sensitivity Analyses Assess Models Uncertainty and Missing Data. 4. Covariation. 4.1 Assessing and Describing Covariation. 4.2 Don’t Summarize Regression Sampling Schemes. 4.3 Do Not Correlate Rates or Ratios Indiscriminately. 4.4 Determining Sample Size to Estimate a Correlation. 4.5 Pairing Data is not Always Good. 4.6 Go Beyond Correlation in Drawing Conclusions. 4.7 Agreement As Accuracy, Scale Differential, and Precision. 4.8 Assess Test Reliability by Means of Agreement. 4.9 Range of the Predictor Variable and Regression. 4.10 Measuring Change: Width More Important than Numbers. 5. Environmental Studies. 5.1 Begin with the Lognormal Distributions in Environmental Studies. 5.2 Differences Are More Symmetrical. 5.3 Know the Sample Space for Statements of Risk. 5.4 Beware of Pseudoreplication. 5.5 Think Beyond Simple Random Sampling. 5.6 The Size of the Population and Small Effects. 5.7 Models of Small Effects Are Sensitive to Assumptions. 5.8 Distinguish Between Variability and Uncertainty. 5.9 Description of the Database is As Important as Its Data. 5.10 Always Assess the Statistical Basis for an Environmental Standard. 5.11 Measurement of a Standard and Policy. 5.12 Parametric Analyses Make Maximum Use of the Data. 5.13 Confidence, Prediction, and Tolerance Intervals. 5.14 Statistics and Risk Assessment. 5.15 Exposure Assessment is the Weak Link in Assessing Health Effects of Pollutants. 5.16 Assess the Errors in Calibration Due to Inverse Regression. 6. Epidemiology. 6.1 Start with the Poisson to Model Incidence or Prevalence. 6.2 The Odds Ratio Approximates the Relative Risk Assuming the Disease is Rare. 6.3 The Number of Events is Crucial in Estimating Sample Size. 6.4 Use a Logarithmic Formulation to Calculate Sample Size. 6.5 Take No More than Four or Five Controls per Case. 6.6 Obtain at Least Ten Subjects for Every Variable Investigated. 6.7 Begin with Two Exponential Distribution to Model Time to Event. 6.8 Begin with Two Exponentials for Comparing Survival Times. 6.9 Be Wary of Surrogates. 6.10 Prevalence Dominates in Screening Rare Diseases. 6.11 Do Not Dichotomize Unless Absolutely Necessary. 6.12 Additive and Multiplicative Models. 7. Evidence-Based Medicine. 7.1 Strength of Evidence. 7.2 Relevance of Information: POEM vs. DOE. 7.3 Begin with Absolute Risk Reduction, then follow with Relative Risk. 7.4 The Number Needed to Treat (NNT) is Clinically Useful. 7.5 Variability in Response to Treatment Needs to be Considered. 7.6 Safety is the Weak Component of EBM. 7.7 Intent to Treat is the Default Analysis. 7.8 Use Prior Information but not Priors. 7.9 The Four Key Questions for Meta-analysis. 8. Design, Conduct, and Analysis. 8.1 Randomization Puts Systematic Effects into the Error Term. 8.2 Blocking is the Key to Reducing Variability. 8.3 Factorial Designs and Joint Effects. 8.4 High-Order Interactions Occur Rarely. 8.5 Balanced Designs Allow Easy Assessment of Joint Effects. 8.6 Analysis Follows Designs. 8.7 Independence, Equal Variance, and Normality. 8.8 Plan to Graph the Results of an Analysis. 8.9 Distinguish Between Design Structure and Treatment Structure. 8.10 Make Hierarchical Analyses the Default Analysis. 8.11 Distinguish Between Nested and Crossed Designs-Not Always Easy. 8.12 Plan for Missing Data. 8.13 Address Multiple Comparisons Before Starting the Study. 8.14 Know Properties Preserved When Transforming Units. 8.15 Consider Bootstrapping for Complex Relationships. 9. Words, Tables, and Graphs. 9.1 Use Text for a Few Numbers, Tables for Many Numbers, Graphs and Complex Relationships. 9.2 Arrange Information in a Table to Drive Home the Message. 9.3 Always Graph the Data. 9.4 Always Graph Results of An Analysis of Variance. 9.5 Never Use a Pie Chart. 9.6 Bar Graphs Waste Ink; They Don’t Illuminate Complex Relationships. 9.7 Stacked Bar Graphs Are Worse Than Bar Graphs. 9.8 Three-Dimensional Bar Graphs Constitute Misdirected Artistry. 9.9 Identify Cross-sectional and Longitudinal Patterns in Longitudinal Data. 9.10 Use Rendering, Manipulation, and Linking in High-Dimensional Data. 10. Consulting. 10.1 Session Has Beginning, Middle, and End. 10.2 Ask Questions. 10.3 Make Distinctions. 10.4 Know Yourself, Know the Investigator. 10.5 Tailor Advice to the Level of the Investigator. 10.6 Use Units the Investigator is Comfortable With. 10.7 Agee on Assignment of Responsibilities. 10.8 Any Basic Statistical Computing Package Will Do. 10.9 Ethics Precedes, Guides, and Follows Consultation. 10.10 Be Proactive in Statistical Consulting. 10.11 Use the Web for Reference, Resource, and Education. 10.12 Listen to, and Heed the Advice of Experts in the Field. Epilogue. Reference. Author Index. Topic Index.

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Book SynopsisParameter Estimation for Scientists and Engineers discusses estimating parameters of expectation models of statistical observations. It aims to show scientists and engineers, who often are not aware of estimators other than least squares, that statistical parameter estimation has much more to offer than least squares estimation alone.Trade Review“An indispensable tool for scholars and research workers in mathematics and the mathematical sciences.” (Mathematical Reviews, 2009) "Despite its lean size, the book is able to cover many of the techniques and theories in parameter estimation that are core to applied sciences, and so this is certainly a valuable reference for researchers and graduate students alike. The book's exposition is lucid, making it an accessible reading for someone with a reasonable background in elementary statistics. Thus I think anyone in applied sciences and engineering dealing with the implementation of expectation models and aiming to estimate model parameters will find this book helpful. This is a great addition to resources in learning or reviewing statistical tools that emphasize taking advantage of valuable information from data and improving the precision of estimation." (Technometrics, November 2008) "I highly recommend this book to practitioners who want to systematically learn and use, new, better techniques for parameter estimation." (Computing Reviews, September 10, 2008) "…appropriate for students in advanced applied statistics courses…even more useful as a supplemental resource…" (CHOICE, January 2008)Table of ContentsPreface. 1 Introduction. 2 Parametric Models of Observations. 3 Distributions of Observations. 4 Precision and Accuracy. 5 Precise and Accurate Estimation. 6 Numerical Methods for Parameter Estimation. 7 Solutions or Partial Solutions to Problems. Appendix A: Statistical Results. Appendix B: Vectors and Matrices. Appendix C: Positive Semidefinite and Positive Definite Matrices. Appendix D: Vector and Matrix Differentiation. References. Topic Index.

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Book SynopsisThe concepts and theorems of advanced calculus combined with related computational methods are essential to understanding nearly all areas of quantitative science. Analysis in Vector Spaces presents the central results of this classic subject through rigorous arguments, discussions, and examples.Trade Review"The authors do not shy away from doing the hard work involved in proving say, the change of variable theorem for integration, the inverse function theorem, and Stokes's theorem--work which is not generally seen in standard calculus books--and thus they are quite correct when they state that students need a firm grip on single-variable calculus and some linear algebra, and a good comfort level with the comprehension and construction of rigorous proofs. Includes many examples and an excellent selection of exercises." (CHOICE, November 2010)Table of ContentsPreface. PART I BACKGROUND MATERIAL. 1 Sets and Functions. 1.1 Sets in General. 1.2 Sets of Numbers. 1.3 Functions. 2 Real Numbers. 2.1 Review of the Order Relations. 2.2 Completeness of Real Numbers. 2.3 Sequences of Real Numbers. 2.4 Subsequences. 2.5 Series of Real Numbers. 2.6 Intervals and Connected Sets. 3 Vector Functions. 3.1 Vector Spaces: The Basics. 3.2 Bilinear Functions. 3.3 Multilinear Functions. 3.4 Inner Products. 3.5 Orthogonal Projections. 3.6 Spectral Theorem. PART II DIFFERENTIATION. 4 Normed Vector Spaces. 4.1 Preliminaries. 4.2 Convergence in Normed Spaces. 4.3 Norms of Linear and Multilinear Transformations. 4.4 Continuity in Normed Spaces. 4.5 Topology of Normed Spaces. 5 Derivatives. 5.1 Functions of a Real Variable. 5.2 Differentiable Functions. 5.3 Existence of Derivatives. 5.4 Partial Derivatives. 5.5 Rules of Differentiation. 5.6 Differentiation of Products. 6 Diffeomorphisms and Manifolds. 6.1 The Inverse Function Theorem. 6.2 Graphs. 6.3 Manifolds in Parametric Representations. 6.4 Manifolds in Implicit Representations. 6.5 Differentiation on Manifolds. 7 Higher-Order Derivatives. 7.1 Definitions. 7.2 Change of Order in Differentiation. 7.3 Sequences of Polynomials. 7.4 Local Extremal Values. PART III INTEGRATION. 8 Multiple Integrals. 8.1 Jordan Sets and Volume. 8.2 Integrals. 8.3 Images of Jordan Sets. 8.4 Change of Variables. 9 Integration on Manifolds. 9.1 Euclidean Volumes. 9.2 Integration on Manifolds. 9.3 Oriented Manifolds. 9.4 Integrals of Vector Fields. 9.5 Integrals of Tensor Fields. 9.6 Integration on Graphs. 10 Stokes’ Theorem. 10.1 Basic Stokes’ Theorem. 10.2 Flows. 10.3 Flux and Change of Volume in a Flow. 10.4 Exterior Derivatives. 10.5 Regular and Almost Regular Sets. 10.6 Stokes’ Theorem on Manifolds. PART IV APPENDICES. Appendix A: Construction of the Real Numbers. A.1 Field and Order Axioms in Q. A.2 Equivalence Classes of Cauchy Sequences in Q. A.3 Completeness of R. Appendix B: Dimension of a Vector Space. B.1 Bases and Linearly Independent Subsets. Appendix C: Determinants. C.1 Permutations. C.2 Determinants of Square Matrices. C.3 Determinant Functions. C.4 Determinant of a Linear Transformation. C.5 Determinants on Cartesian Products. C.6 Determinants in Euclidean Spaces. C.7 Trace of an Operator. Appendix D: Partitions of Unity. D.1 Partitions of Unity. Index.

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Book SynopsisIn order to assess the effectiveness of groundwater monitoring devices and their end results, statistical techniques must be employed. Thoroughly updated and expanded, the Second Edition examines the multiple problems inherent in the analysis of groundwater monitoring data and illustrates their application and interconnections.Trade Review"This book is an excellent supplementary text for courses on environmental statistics or reference for researchers and practitioners." (Book News, December 2009)Table of ContentsPreface xv Acknowledgments xxiii Acronyms xxv 1 Normal Prediction Intervals 1 2 Nonparametric Prediction Intervals 35 3 Prediction Intervals for Other Distributions 67 4 Gamma Prediction Intervals and Some Related Topics 77 5 Tolerance Intervals 97 6 Method Detection Limits 111 7 Practical Quantitation Limits 137 8 Interlaboratory Calibration 147 9 Contaminant Source Analysis 161 10 Intra-Well Comparison 191 11 Trend Analysis 205 12 Censored Data 217 13 Normal Prediction Limits for Left-Censored Data 245 14 Tests for Departure From Normality 257 15 Variance Component Models 281 16 Detecting Outliers 289 17 Surface Water Analysis 303 18 Assessment and Corrective Action Monitoring 317 19 Regulatory Issues 337 20 Summary 351 Topic Index 366

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Book SynopsisThis book covers the most common methods for analyzing single, double, three-way and multi-way data. Geared towards applications and the decisions that have to be made to get meaningful analyses, it presents a variety of models illustrated using commercially available software.Trade Review"All topics are well illustrated with good examples from a fairly wide range of applications... the book’s usefulness is enhanced by a glossary of multiway terminology, a good index and references to extension work... this is a well-crafted and highly readable book." (Journal of the Royal Statistical Society- Series A, 2009) “All in all, Kroonenberg’s book constitutes an extremely valuable tool for applied researchers in almost all domains of investigation, whenever they are faced with the task of analyzing complex statistical data in view of obtaining useful information in their areas of interest.” (Biometrics, June 2009) “This book is focused primarily toward graduate students in the areas of chemistry, social and behavioral sciences, and environmental sciences, although the techniques and methods used can be more broadly used in other areas, such as finance and engineering, as well.” (Technometrics, May 2009) "Kroonenberg’s book constitutes an extremely valuable tool for applied researchers in almost all domains of investigation (from economics to psychology, from biomedicine to technology and physical sciences), whenever they are faced with the task of analyzing complex statistical data in view of obtaining useful information in their areas of interest." (Biometrics 2009) "...the combination of worked-out examples alongside descriptions and critical considerations on the theory behind those analyses make AMDA an interesting book for researchers and practitioners in both academia and industry. (Journal of the American Statistical Association 2009) "The book is written in a clear style and mostly in conceptual rather than mathematical level. It emphasized the author's over thirty years' personal experience and practical side of performing multiway data analyses. It is easy to recommend this book, as it really open news views of the world." (International Statistical Review, December 2008) "Good things take time - and this hold for this book as well…Pieter Kroonenberg is one of the few with a profound knowledge of multiway analysis. It is meritorious that he took the effort to share his knowledge. It is to be hoped that a next edition will appear soon...the book deserves a broad reading public." (Vereniging voor Ordinatie en Classificatie, Nieuwsbrief, no 41, November 2008) "We believe that this book will offer applied researchers a lot of good advice for using three-way techniques. In addition, Applied Multiway Data-Analysis will turn out to be a valuable resource of reference for three-way specialists." (Mathematical Reviews, 2008)Table of ContentsForeword xv Preface xvii PART I DATA, MODELS, AND ALGORITHMS 1 Overture 3 1.1 Three-way and multiway data 4 1.2 Multiway data analysis 5 1.3 Before the arrival of three-mode analysis 6 1.4 Three-mode data-analytic techniques 7 1.5 Example: Judging Chopin's preludes 7 1.6 Birth of the Tucker model 12 1.7 Current status of multiway analysis 12 2 Overview 15 2.1 What are multiway data? 15 2.2 Why multiway analysis? 17 2.3 What is a model? 18 2.4 Some history 20 2.5 Multiway models and methods 24 2.6 Conclusions 24 3 Three-Way and Multiway Data 27 3.1 Chapter preview 27 3.2 Terminology 28 3.3 Two-way solutions to three-way data 30 3.4 Classification principles 31 3.5 Overview of three-way data designs 33 3.6 Fully crossed designs 33 3.7 Nested designs 38 3.8 Scaling designs 40 3.9 Categorical data 41 4 Component Models for Fully-Crossed Designs 43 4.1 Introduction 43 4.2 Chapter preview 45 4.3 Two-mode modeling of three-way data 45 4.4 Extending two-mode component models to three-mode models 47 4.5 Tucker models 51 4.6 Parafac models 57 4.7 ParaTuck2 model 63 4.8 Core arrays 64 4.9 Relationships between component models 66 4.10 Multiway component modeling under constraints 68 4.11 Conclusions 74 5 Algorithms for Multiway Models 77 5.1 Introduction 77 5.2 Chapter preview 78 5.3 Terminology and general issues 79 5.4 An example of an iterative algorithm 81 5.5 General behavior of multiway algorithms 84 5.6 The Parallel factor model - Parafac 85 5.7 The Tucker models 97 5.8 STATIS 105 5.9 Conclusions 106 PART II DATA HANDLING, MODEL SELECTION, AND INTERPRETATION 6 Preprocessing 109 6.1 Introduction 109 6.2 Chapter preview 112 6.3 General considerations 112 6.4 Model-based arguments for preprocessing choices 117 6.5 Content-based arguments for preprocessing choices 128 6.6 Preprocessing and specific multiway data designs 130 6.7 Centering and analysis-of-variance models: Two-way data 134 6.8 Centering and analysis-of-variance models: Three-way data 137 6.9 Recommendations 141 7 Missing Data in Multiway Analysis 143 7.1 Introduction 143 7.2 Chapter preview 147 7.3 Handling missing data in two-mode PCA 148 7.4 Handling missing data in multiway analysis 154 7.5 Multiple imputation in multiway analysis: Data matters 156 7.6 Missing data in multiway analysis: Practice 157 7.7 Example: Spanjer's Chromatography data 159 7.8 Example: NICHD Child care data 168 7.9 Further applications 172 7.10 Computer programs for multiple imputation 174 8 Model and Dimensionality Selection 175 8.1 Introduction 175 8.2 Chapter preview 176 8.3 Sample size and stochastics 176 8.4 Degrees of freedom 177 8.5 Selecting the dimensionality of a Tucker model 179 8.6 Selecting the dimensionality of a Parafac model 184 8.7 Model selection from a hierarchy 186 8.8 Model stability and predictive power 187 8.9 Example: Chopin prelude data 190 8.10 Conclusions 208 9 Interpreting Component Models 209 9.1 Chapter preview 209 9.2 General principles 210 9.3 Representations of component models 215 9.4 Scaling of components 218 9.5 Interpreting core arrays 225 9.6 Interpreting extended core arrays 231 9.7 Special topics 232 9.8 Validation 233 9.9 Conclusions 235 10 Improving Interpretation through Rotations 237 10.1 Introduction 237 10.2 Chapter preview 240 10.3 Rotating components 241 10.4 Rotating full core arrays 244 10.5 Theoretical simplicity of core arrays 254 10.6 Conclusions 256 11 Graphical Displays for Components 257 11.1 Introduction 257 11.2 Chapter preview 258 11.3 General considerations 259 11.4 Plotting single modes 260 11.5 Plotting different modes together 270 11.6 Conclusions 279 12 Residuals, Outliers, and Robustness 281 12.1 Introduction 281 12.2 Chapter preview 282 12.3 Goals 283 12.4 Procedures for analyzing residuals 284 12.5 Decision schemes for analyzing multiway residuals 287 12.6 Structured squared residuals 287 12.7 Unstructured residuals 292 12.8 Robustness: Basics 294 12.9 Robust methods of multiway analysis 297 12.10 Examples 301 12.1 1 Conclusions 307 PART III MULTIWAY DATA AND THEIR ANALYSIS 13 Modeling Multiway Profile Data 311 13.1 Introduction 311 13.2 Chapter preview 313 13.3 Example: Judging parents' behavior 313 13.4 Multiway profile data: General issues 320 13.5 Multiway profile data: Parafac in practice 322 13.6 Multiway profile data: Tucker analyses in practice 331 13.7 Conclusions 342 14 Modeling Multiway Rating Scale Data 345 14.1 Introduction 345 14.2 Chapter preview 346 14.3 Three-way rating scale data: Theory 346 14.4 Example: Coping at school 354 14.5 Analyzing three-way rating scales: Practice 360 14.6 Example: Differences within a multiple personality 361 14.7 Conclusions 370 15 Exploratory Multivariate Longitudinal Analysis 373 15.1 Introduction 373 15.2 Chapter preview 375 15.3 Overview of longitudinal modeling 375 15.4 Longitudinal three-mode modeling 378 15.5 Example: Organizational changes in Dutch hospitals 385 15.6 Example: Morphological development of French girls 394 15.7 Further reading 400 15.8 Conclusions 401 16 Three-Mode Clustering 403 16.1 Introduction 403 16.2 Chapter preview 405 16.3 Three-mode clustering analysis: Theory 405 16.4 Example: Identifying groups of diseased blue crabs 409 16.5 Three-mode cluster analysis: Practice 411 16.6 Example: Behavior of children in the Strange Situation 424 16.7 Extensions and special topics 430 16.8 Conclusions 432 17 Multiway Contingency Tables 433 17.1 Introduction 433 17.2 Chapter preview 434 17.3 Three-way correspondence analysis: Theory 435 17.4 Example: Sources of happiness 444 17.5 Three-way correspondence analysis: Practice 448 17.6 Example: Playing with peers 454 17.7 Conclusions 458 18 Three-Way Binary Data 459 18.1 Introduction 459 18.2 Chapter preview 460 18.3 A graphical introduction 460 18.4 Formal description of the Tucker-HICLAS models 462 18.5 Additional issues 465 18.6 Example: Hostile behavior in frustrating situations 465 18.7 Conclusion 467 19 From Three-Way Data to Four-Way Data and Beyond 469 19.1 Introduction 469 19.2 Chapter preview 471 19.3 Examples of multiway data 471 19.4 Multiway techniques: Theory 474 19.5 Example: Differences within a multiple personality 476 19.6 Example: Austrian aerosol particles 480 19.7 Further reading and computer programs 487 19.8 Conclusions 488 Appendix A: Standard Notation for Multiway Analysis 489 Appendix B: Biplots and Their Interpretation 491 B. 1 Introduction 492 B.2 Singular value decomposition 492 B.3 Biplots 494 B.4 Relationship with PCA 499 B.5 Basic vector geometry relevant to biplots 499 References 501 Glossary 527 Acronyms 543 Author Index 545 Subject Index 553

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Book SynopsisA thorough and self-contained treatment for SAS users preparing for the Base Programming Certification Exam for SAS 9complete with explanations, tips, and practice exam questions SAS 9 Study Guide is designed to help users of SAS 9 become familiar with the fine points of the software as well as develop solid study strategies that will shorten preparation time and ensure successful exam results. The following five study topics are addressed with a focused chapter devoted to each: accessing data; creating data structures; managing data; generating reports; and handling errors. SAS 9 Study Guide provides both a conceptual and practical approach to each of these areas with detailed explanations followed by examples. Each chapter presents concepts, processes, and applications in a clear, step-by-step format along with detailed explanations and examples. Individual chapters also contain: A Two-Minute Drill that provides a checklist of key points for review Table of ContentsContents. Preface. Introduction. I. Accessing Data. 2. Creating Data Structures. 3. Managing Data. 4. Generating Reports. 5. Handling Errors. Index.

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Book SynopsisThe first all-inclusive introduction to modern statistical research methods in the natural resource sciences he use of Bayesian statistical analysis has become increasingly important to natural resource scientists as a practical tool for solving various research problems.Trade Review?The book provides case studies illustrating common problems that exist in natural resource sciences, and presents the statistical knowledge and tools needed for a modem treatment of these issues.? (APADE, 2009) "The book's strength lie in the choice of material, the explication of methods and use, and detail of the code provided ? The bottom line is this book is useful. It is designated not merely to give you a sense of these often-neglected statistical methods but to get you up and running on them. It does a phenomenal job of that task." (Ecology, November 2008) "Stauffer's book seems very suitable for second statistics on modern regression modeling focusing on Bayesian thinking." (Journal of the American Statistician, December 2008) "Stauffer's book seems very suitable for second statistics on modern regression modeling focusing on Bayesian thinking." (Journal of the American Statistician, Dec 2008) "This is an excellent book presenting difficult statistical ideals by using data obtained from real-life situations." (CHOICE May 2008) "An ideal text for natural resource students studying statistical research methods at the upper-undergraduate or graduate level and also service as a valuable problem-solving guide." (Mathematical Reviews 2008)Table of ContentsPreface. 1. Introduction. 1.1 Introduction. 1.2 Three Case Studies. 1.3 Overview of Some Solution Strategies. 1.4 Review: Principles of Project Management. 1.5 Applications. 1.6 S-Plus ® and R Orientation I: Introduction. 1.7 S-Plus and R Orientation II: Distributions. 1.8 S-Plus and R Orientation III: Estimation of Mean and Proportion, Sampling Error, and Confidence Intervals. 1.9 S-Plus and R Orientation IV: Linear Regression. 1.10 Summary. Problems. 2. Bayesian Statistical Analysis I: Introduction. 2.1 Introduction. 2.2 Three Methods for Fitting Models to Datasets. 2.3 The Bayesian Paradigm for Statistical Inference: Bayes Theorem. 2.4 Conjugate Priors. 2.5 Other Priors. 2.6 Summary. Problems. 3. Bayesian Statistical Inference II: Bayesian Hypothesis Testing and Decision theory. 3.1 Bayesian Hypothesis Testing: Bayes Factors. 3.2 Bayesian Decision Theory. 3.3 Preview: More Advanced Methods of Bayesian Statistical Analysisâ??Markov Chain Monte Carlo (MCMC) Algorithms and WinBUGS Software. 3.4 Summary. Problems. 4. Bayesian Statistical Inference III: MCMC Algorithms and WinBUGS Software Applications. 4.1 Introduction. 4.2 Markov Chain Theory. 4.3 MCMC Algorithms. 4.4 WinBUGS Applications. 4.5 Summary. Problems. 5. Alternative Strategies for Model Selection and Inference Using Information-Theoretic Criteria. 5.1 Alternative Strategies for Model Selection and Influence: Descriptive and Predictive Model Selection. 5.2 Descriptive Model Selection: A Posteriori Exploratory Model Selection and Inference. 5.3 Predictive Model Selection: A Priori Parsimonious Model Selection and Inference Using Information-Theoretic Criteria. 5.4 Methods of Fit. 5.5 Evaluation of Fit: Goodness of Fit. 5.6 Model Averaging. 5.7 Applications: Frequentist Statistical Analysis in S-Plus and R; Bayesian Statistical Analysis in WinBUGS. 5.8 Summary. Problems. 6. An Introduction to Generalized Linear Models: Logistic Regression Models. 6.1 Introduction to Generalized Linear Models (GLMs). 6.2 GLM Design. 6.3 GLM Analysis. 6.4 Logistic Regression Analysis. 6.5 Other Generalized Linear Models (GLMs). 6.6 S-Plus or R and WinBUGS Applications. 6.7 Summary. Problems. 7. Introduction to Mixed-Effects Modeling. 7.1 Introduction. 7.2 Dependent Datasets. 7.3 Linear Mixed-Effects Modeling: Frequentist Statistical Analysis in S-Plus and R. 7.4 Nonlinear Mixed-Effects Modeling: Frequentist Statistical Analysis in S-Plus and R. 7.5 Conclusions: Frequentist Statistical Analysis in S-Plus and R. 7.6 Mixed-Effects Modeling: Bayesian Statistical Analysis in WinBUGS. 7.7 Summary. Problems. 8. Summary and Conclusions. 8.1 Summary of Solutions to Chapter 1 Case Studies. 8.2 Appropriate Application of Statistics in the Natural Resource Sciences. 8.3 Statistical Guidelines for Design of Sample Surveys and Experiments. 8.4 Two Strategies for Model Selection and Inference. 8.5 Contemporary Methods for Statistical Analysis I: Generalized Linear Modeling and Mixed-Effects Modeling. 8.6 Contemporary Methods in Statistical Analysis II: Bayesian Statistical Analysis Using MCMC Methods with WinBUGS Software. 8.7 Concluding Remarks: Effective Use of Statistical Analysis and Inference. 8.8 Summary. Appendix A. review of Linear regression and Multiple Linear regression Analysis. A.1 Introduction. A.2 Least-Square Fit: The Linear Regression Model. A.3 Linear Regression and Multiple Linear Regression Statistics. A.4 Stepwise Multiple Linear Regression Methods. A.5 Best-Subsets Selection Multiple Linear Regression. A.6 Goodness of Fit. Appendix B. Answers to Problems. References. Index.

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Book SynopsisBased on the premise that in order to write proofs, one needs to read finished proofs as well as study both their logic and grammar, Revolutions in Geometry depicts how to write basic proofs in various fields of geometry.Trade Review"An excellent supplemental resource or main textbook for an overview of mathematics course for upper-level undergraduate and graduate students." (Choice, October 2010).Table of ContentsPreface. Acknowledgments. PART I FOUNDATIONS. 1 The First Geometers. 1.1 Egypt. 1.2 Babylon. 1.3 China. 2 Thales. 2.1 The Axiomatic System. 2.2 Deductive Logic. 2.3 Proof Writing. 3 Plato and Aristotle. 3.1 Form. 3.2 Categorical Propositions.. 3.3 Categorical Syllogisms. 3.4 Figures. PART II THE GOLDEN AGE. 4 Pythagoras. 4.1 Number Theory. 4.2 The Pythagorean Theorem. 4.3 Archytas. 4.4 The Golden Ratio. 5 Euclid. 5.1 The Elements. 5.2 Constructions. 5.3 Triangles. 5.4 Parallel Lines. 5.5 Circles. 5.6 The Pythagorean Theorem Revisited. 6 Archimedes. 6.1 The Archimedean Library. 6.2 The Method of Exhaustion. 6.3 The Method. 6.4 Preliminaries to the Proof. 6.5 The Volume of a Sphere. PART III ENLIGHTENMENT. 7 François Viète. 7.1 The Analytic Art. 7.2 Three Problems. 7.3 Conic Sections. 7.4 The Analytic Art in Two Variables. 8 René Descartes. 8.1 Compasses. 8.2 Method. 8.3 Analytic Geometry. 9 Gérard Desargues. 9.1 Projections. 9.2 Points at Infinity. 9.3 Theorems of Desargues and Menelaus. 9.4 Involutions. PART IV A STRANGE NEW WORLD. 10 Giovanni Saccheri. 10.1 The Question of Parallels. 10.2 The Three Hypotheses. 10.3 Conclusions for Two Hypotheses. 10.4 Properties of Parallel Lines. 10.5 Parallelism Redefined. 11 Johann Lambert. 11.1 The Three Hypotheses Revisited. 11.2 Polygons. 11.3 Omega Triangles. 11.4 Pure Reason. 12 Nicolai Lobachevski and János Bolyai. 12.1 Parallel Fundamentals. 12.2 Horocycles. 12.3 The Surface of a Sphere. 12.4 Horospheres. 12.5 Evaluating the Pi Function. PART V NEW DIRECTIONS. 13 Bernhard Riemann. 13.1 Metric Spaces. 13.2 Topological Spaces. 13.3 Stereographic Projection. 13.4 Consistency of Non-Euclidean Geometry. 14 Jean-Victor Poncelet. 14.1 The Projective Plane. 14.2 Duality. 14.3 Perspectivity. 14.4 Homogeneous Coordinates. 15 Felix Klein. 15.1 Group Theory. 15.2 Transformation Groups. 15.3 The Principal Group. 15.4 Isometries of the Plane. 15.5 Consistency of Euclidean Geometry. References. Index.

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