Probability and statistics Books

Book SynopsisMachine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical tools that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in econometrics textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targTable of Contents1. How We Define Machine Learning 2. Preliminaries Part 1. Formal Look at Prediction 3. Bias-Variance Tradeoff 4. Overfitting Part 2. Nonparametric Estimations 5. Parametric Estimations 6. Nonparametric Estimations - Basics 7. Smoothing 8. Nonparametric Classifier - kNN Part 3. Self-learning 9. Hyperparameter Tuning 10. Tuning in Classification 11. Classification Example Part 4. Tree-based Models 12. CART 13. Ensemble Learning 14. Ensemble Applications Part 5. SVM & Neural Networks 15. Support Vector Machines 16. Artificial Neural Networks Part 6. Penalized Regressions 17. Ridge 18. Lasso 19. Adaptive Lasso 20. Sparsity Part 7. Time Series Forecasting 21. ARIMA models 22. Grid Search for Arima 23. Time Series Embedding 24. Random Forest with Times Series 25. Recurrent Neural Networks Part 8. Dimension Reduction Methods 26. Eigenvectors and eigenvalues 27. Singular Value Decomposition 28. Rank r approximations 29. Moore-Penrose Inverse 30. Principle Component Analysis 31. Factor Analysis Part 9. Network Analysis 32. Fundamentals 33. Regularized Covariance Matrix Part 10. R Labs 34. R Lab 1 Basics 35. R Lab 2 Basics II 36. Simulations in R 37. Algorithmic Optimization 38. Imbalanced Data

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Book SynopsisThis second edition of Lessons in Play reorganizes the presentation of the popular original text in combinatorial game theory to make it even more widely accessible. Starting with a focus on the essential concepts and applications, it then moves on to more technical material. Still written in a textbook style with supporting evidence and proofs, the authors add many more exercises and examples and implement a two-step approach for some aspects of the material involving an initial introduction, examples, and basic results to be followed later by more detail and abstract results.Features Employs a widely accessible style to the explanation of combinatorial game theory Contains multiple case studies Expands further directions and applications of the field IncluTrade Review"The wisdom and joy outshining from this 2nd edition, beat even the original. The helpful preludes for student and instructor, prefacing each chapter, have elevated subtly in additional reader-friendliness; new subsections and a new case study were added. An interesting new Chapter 10 trades complex yet complete computation of a game’s strategy, with a simplified slightly approximate winning strategy. The last chapter, which awards the reader with a flavor of cutting edge research, was updated with a section on scoring games. The book is a must for novice and expert alike." —Aviezri Fraenkel, Weizmann Institute of Science, Israel "In this second edition of Lessons in Play, the authors have corrected errors, updated the bibliography, and added a new chapter on trimming game trees. Like the first edition, this new edition is beautifully typeset and illustrated." —Brian Borchers, Editor, MAA Reviews In this second edition of Lessons in Play: An Introduction to Combinatorial Game Theory, authors Albert , Nowakowski, and White provide a reorganized text presenting a variety of two-player finite games, discussed in theory as well as application. The theoretical material is presented in a clear and concise theorem/proof format and includes problems and exercises to aid readers’ understanding. Solutions are provided at the end of the book. Multiple examples from actual games are provided throughout, including Boxcars, Clobber, Cutthroat, Dots and Boxes, Hackenbush, and Toppling Dominoes. Throughout the text, the authors also provide in-depth case studies on specific games. A unique feature of this book is that each chapter begins by presenting a series of “prep problems” with notes to the instructor so students can preview the material prior to reading the chapter. Overall, this book is an excellent beginning read for anyone interested in learning about combinatorial games, assuming at least some background in abstract algebra. —S. L. Sullivan, Catawba College Praise for the previous edition This is an excellent introductory book to beginning game theory, written in an easily understandable manner yet advanced enough not to be considered trivial.—Books Online, July 2007 The first book to present combinatorial game theory in the form of a textbook suitable for students at the advanced undergraduate level … The authors state and prove theorems in a rigorous fashion [and] the presentation is enlivened with many concrete examples … an outstanding textbook … It will also be of interest to more advanced readers who want an introduction to combinatorial game theory.—Brian Borchers, June 2007 The theory is accessible to any student who has a smattering of general algebra and discrete math. Generally, a third year college student, but any good high school student should be able to follow the development with a little help.—Sir Read a Lot, May 2007 Lessons in Play is an enticing introduction to the wonderful world of combinatorial games. Using a rich collection of cleverly captivating examples and problems, the authors lead the reader through the basic concepts and on to several innovative extensions. I highly recommend this book.—Elwyn R. Berlekamp A neat machine, converting novices into enthusiastic experts in modern combinatorial game theory.—Aviezri Fraenkel Combinatorial games are intriguing, challenging, and often counter-intuitive, and are rapidly being recognized as an important mathematical discipline. Now that we have the attractive and friendly text Lessons in Play in hand, we can look forward to the appearance of many popular upper-division undergraduate courses, which encourage instructors to learn alongside their students.—Richard K. Guy … If you have Winning Ways, you must have this book.—Andy Liu "The wisdom and joy outshining from this 2nd edition, beat even the original. The helpful preludes for student and instructor, prefacing each chapter, have elevated subtly in additional reader-friendliness; new subsections and a new case study were added. An interesting new Chapter 10 trades complex yet complete computation of a game’s strategy, with a simplified slightly approximate winning strategy. The last chapter, which awards the reader with a flavor of cutting edge research, was updated with a section on scoring games. The book is a must for novice and expert alike." —Aviezri Fraenkel, Weizmann Institute of Science, Israel "In this second edition of Lessons in Play, the authors have corrected errors, updated the bibliography, and added a new chapter on trimming game trees. Like the first edition, this new edition is beautifully typeset and illustrated." —Brian Borchers, Editor, MAA Reviews In this second edition of Lessons in Play: An Introduction to Combinatorial Game Theory, authors Albert , Nowakowski, and White provide a reorganized text presenting a variety of two-player finite games, discussed in theory as well as application. The theoretical material is presented in a clear and concise theorem/proof format and includes problems and exercises to aid readers’ understanding. Solutions are provided at the end of the book. Multiple examples from actual games are provided throughout, including Boxcars, Clobber, Cutthroat, Dots and Boxes, Hackenbush, and Toppling Dominoes. Throughout the text, the authors also provide in-depth case studies on specific games. A unique feature of this book is that each chapter begins by presenting a series of “prep problems” with notes to the instructor so students can preview the material prior to reading the chapter. Overall, this book is an excellent beginning read for anyone interested in learning about combinatorial games, assuming at least some background in abstract algebra. —S. L. Sullivan, Catawba College Praise for the previous edition This is an excellent introductory book to beginning game theory, written in an easily understandable manner yet advanced enough not to be considered trivial.—Books Online, July 2007 The first book to present combinatorial game theory in the form of a textbook suitable for students at the advanced undergraduate level … The authors state and prove theorems in a rigorous fashion [and] the presentation is enlivened with many concrete examples … an outstanding textbook … It will also be of interest to more advanced readers who want an introduction to combinatorial game theory.—Brian Borchers, June 2007 The theory is accessible to any student who has a smattering of general algebra and discrete math. Generally, a third year college student, but any good high school student should be able to follow the development with a little help.—Sir Read a Lot, May 2007 Lessons in Play is an enticing introduction to the wonderful world of combinatorial games. Using a rich collection of cleverly captivating examples and problems, the authors lead the reader through the basic concepts and on to several innovative extensions. I highly recommend this book.—Elwyn R. Berlekamp A neat machine, converting novices into enthusiastic experts in modern combinatorial game theory.—Aviezri Fraenkel Combinatorial games are intriguing, challenging, and often counter-intuitive, and are rapidly being recognized as an important mathematical discipline. Now that we have the attractive and friendly text Lessons in Play in hand, we can look forward to the appearance of many popular upper-division undergraduate courses, which encourage instructors to learn alongside their students.—Richard K. Guy … If you have Winning Ways, you must have this book.—Andy Liu Table of ContentsCombinatorial Games 0.1 Basic Terminology Problems 1 Basic Techniques 1.1 Greedy 1.2 Symmetry 1.3 Parity 1.4 Give Them Enough Rope! 1.5 Strategy Stealing 1.6 Change the Game! 1.7 Case Study: Long Chains in Dots & Boxes Problems 2 Outcome Classes 2.1 Outcome Functions 2.2 Game Positions and Options 2.3 Impartial Games: Minding Your Ps and Ns 2.4 Case Study: Roll The Lawn 2.5 Case Study: Timber 2.6 Case Study: Partizan Endnim Problems 3 Motivational Interlude: Sums of Games 3.1 Sums 3.2 Comparisons 3.3 Equality and Identity 3.4 Case Study: Domineering Rectangles Problems 4 The Algebra of Games 4.1 The Fundamental Definitions 4.2 Games Form a Group with a Partial Order 4.3 Canonical Form 4.4 Case Study: Cricket Pitch 4.5 Incentives Problems 5 Values of Games 5.1 Numbers 5.2 Case Study: Shove 5.3 Stops 5.4 A Few All-Smalls: Up, Down, and Stars 5.5 Switches 5.6 Case Study: Elephants & Rhinos 5.7 Tiny and Miny 5.8 Toppling Dominoes 5.9 Proofs of Equivalence of Games and Numbers Problems 6 Structure 6.1 Games Born by Day 2 6.2 Extremal Games Born By Day n 6.3 More About Numbers 6.4 The Distributive Lattice of Games Born by Day n 6.5 Group Structure Problems 7 Impartial Games 7.1 A Star-Studded Game 7.2 The Analysis of Nim 7.3 Adding Stars 7.4 A More Succinct Notation 7.5 Taking-and-Breaking Games 7.6 Subtraction Games 7.7 Keypad Games Problems 8 Hot Games 8.1 Comparing Games and Numbers 8.2 Coping with Confusion 8.3 Cooling Things Down 8.4 Strategies for Playing Hot Games 8.5 Norton Products Problems 9 All-Small Games 9.1 Cast of Characters 9.2 Motivation: The Scale of Ups 9.3 Equivalence Under 9.4 Atomic Weight 9.5 All-Small Shove 9.6 More Toppling Dominoes 9.7 Clobber Problems 10 Trimming Game Trees 10.1 Introduction 10.2 Reduced Canonical Form 10.3 Hereditary-Transitive Games 10.4 Ordinal Sum 10.5 Stirling-Shave 10.6 Even More Toppling Dominoes Problems Further Directions 1 Transfinite Games 2 Algorithms and Complexity 3 Loopy Games 4 Kos: Repeated Local Positions 5 Top-Down Thermography 6 Enriched Environments 7 Idempotents 8 Mis`ere Play 9 Scoring Games A Top-Down Induction A.1 Top-Down Induction A.2 Examples

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Book SynopsisCan you really keep your eye on the ball? How is massive data collection changing sports?Sports science courses are growing in popularity. The author's course at Roanoke College is a mix of physics, physiology, mathematics, and statistics. Many students of both genders find it exciting to think about sports. Sports problems are easy to create and state, even for students who do not live sports 24/7. Sports are part of their culture and knowledge base, and the opportunity to be an expert on some area of sports is invigorating. This should be the primary reason for the growth of mathematics of sports courses: the topic provides intrinsic motivation for students to do their best work.From the Author:The topics covered in Sports Science and Sports Analytics courses vary widely. To use a golfing analogy, writing a book like this is like hitting a drive at a driving range; there are many Trade ReviewThe book is written at a level that is accessible to a large audience. It contains a small number of applications that make use of calculus; otherwise, only a high school level mathematics background is required. Furthermore, one can easily skip over those sections that require calculus and still have plenty of accessible material to read.Sports Math is well written and easy to read. The book should appeal to anyone interested in the quantitative aspects of athletics. Each chapter of the books ends with a fairly large number of exercises and also pointers to further reading. Thus, the book could be used not only as a textbook for a course but also as a nice resource for student projects.~Mathematical Reviews, 2017Minton presents a textbook based on the current status of a sport science course that has evolved since he began teaching it in 1988. He offers a sample of topics that he knows something about and finds interesting, and hopes that instructors and students will find the book useful. His topics are projectile motion, rotational motion, sports illusions, collisions, ratings systems, voting systems, saber- and other metrics, randomness in sports, sports strategies, and big data and beyond.~ProtoView, 2017 This work discusses how mathematics is used to analyze popular American sports like football, baseball, and basketball. Minton (mathematics, Roanoke College) has based this book on several of his undergraduate courses. The book covers two major aspects: the physics involved in sports (e.g., the motion of a ball) and the statistics used to make probabilistic ratings of performance and success. The beginning chapters consider topics from mechanics, such as “Projectile Motion,” “Rotational Motion,” and “Collisions.” The rest of the text is devoted to statistics used in sports ratings and analysis, with many examples from specific games played in the big leagues or by major colleges. The material covered is selective and quirky; the level of analytical mathematics and statistics ranges from simple to advanced, including calculus, matrixes, and game theory. Each chapter has solved examples and end-of-chapter questions, problems, and suggestions for projects. There are pictures and graphs interspersed throughout the text. The book is not suitable as a standard text in any conventional course—it will best serve as a supplement.--N. Sadanand, Central Connecticut State University 2018The book is written at a level that is accessible to a large audience. It contains a small number of applications that make use of calculus; otherwise, only a high school level mathematics background is required. Furthermore, one can easily skip over those sections that require calculus and still have plenty of accessible material to read.Sports Math is well written and easy to read. The book should appeal to anyone interested in the quantitative aspects of athletics. Each chapter of the books ends with a fairly large number of exercises and also pointers to further reading. Thus, the book could be used not only as a textbook for a course but also as a nice resource for student projects.~Mathematical Reviews, 2017Minton presents a textbook based on the current status of a sport science course that has evolved since he began teaching it in 1988. He offers a sample of topics that he knows something about and finds interesting, and hopes that instructors and students will find the book useful. His topics are projectile motion, rotational motion, sports illusions, collisions, ratings systems, voting systems, saber- and other metrics, randomness in sports, sports strategies, and big data and beyond.~ProtoView, 2017This work discusses how mathematics is used to analyze popular American sports like football, baseball, and basketball. Minton (mathematics, Roanoke College) has based this book on several of his undergraduate courses. The book covers two major aspects: the physics involved in sports (e.g., the motion of a ball) and the statistics used to make probabilistic ratings of performance and success. The beginning chapters consider topics from mechanics, such as “Projectile Motion,” “Rotational Motion,” and “Collisions.” The rest of the text is devoted to statistics used in sports ratings and analysis, with many examples from specific games played in the big leagues or by major colleges. The material covered is selective and quirky; the level of analytical mathematics and statistics ranges from simple to advanced, including calculus, matrixes, and game theory. Each chapter has solved examples and end-of-chapter questions, problems, and suggestions for projects. There are pictures and graphs interspersed throughout the text. The book is not suitable as a standard text in any conventional course—it will best serve as a supplement.--N. Sadanand, Central Connecticut State University 2018Table of ContentsProjectile Motion. Rotational Motion. Sports Illusions. Collisions. Rating Systems. Voting Systems. Saber- and Other Metrics. Randomness in Sports. In-Game Strategies. Predictive Analytics.

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Book SynopsisIncorporating a collection of recent results, Pólya Urn Models deals with discrete probability through the modern and evolving urn theory and its numerous applications. The book first substantiates the realization of distributions with urn arguments and introduces several modern tools, including exchangeability and stochastic processes via urns. It reviews classical probability problems and presents dichromatic Pólya urns as a basic discrete structure growing in discrete time. The author then embeds the discrete Pólya urn scheme in Poisson processes to achieve an equivalent view in continuous time, provides heuristical arguments to connect the Pólya process to the discrete urn scheme, and explores extensions and generalizations. He also discusses how functional equations for moment generating functions can be obtained and solved. The final chapters cover applications of urns to computer science and bioscience. Examining how urns can help conceptualize discrete probability Trade Review"Pólya Urn Models describes the established theory and details numerous new developments since the seminal 1977 text Urn Models and Their Application: An Approach to Modern Discrete Probability Theory by Johnson and Kotz. … the second half of Mahmoud’s book largely covers new advances since Johnson and Kotz’s book … This latter material makes the current text unique. … The end-of-chapter exercises (of greatly varying difficulty) have excellent in-depth solutions at the end of the text. … The book is exceptionally well written, concise, and compelling. The author obviously commands this material, and his enthusiasm is unmistakable."—Matthew Bognar, Journal of the American Statistical Association, September 2014, Vol. 109Table of ContentsUrn Models and Rudiments of Discrete Probability. Some Classical Urn Problems.Pólya Urn Models.Poissonization. The Depoissonization Heuristic. Urn Schemes with Random Replacement.Analytic Urns.Applications of Pólya Urns in Informatics.Urn Schemes in Bioscience.Urns Evolving by Multiple Drawing. Answers to Exercises. Notation. Bibliographic Notes. Bibliography. Index.

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Book SynopsisThe Likelihood plays a key role in both introducing general notions of statistical theory, and in developing specific methods. This book introduces likelihood-based statistical theory and related methods from a classical viewpoint, and demonstrates how the main body of currently used statistical techniques can be generated from a few key concepts, in particular the likelihood.Focusing on those methods, which have both a solid theoretical background and practical relevance, the author gives formal justification of the methods used and provides numerical examples with real data.Trade Review"...this book reads well, and it is a welcome addition to the literature. I recommend its use as a text for an introductory graduate level course."-JASA"...provides numerical answers with real data."-L'Enseignment Mathematique"From the preface: 'the aim is to show how the main body of currently used statistical techniques can be generated from a few key concepts, in particular the likelihood'. The author achieves this aim extremely well."-Publication of the International Statistical InstituteTable of ContentsStatistical Inference Based on the likelihood

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Book SynopsisInterpreting poll data as a prediction of election outcomes is a practice as old as the field, rooted in a fundamental misunderstanding of what poll data means.By first understanding how polls work at a fundamental level, this book gives readers the ability to discern flaws in the current methods. Then, through specific political examples from both the United States and the United Kingdom, it is shown how polls famously derided as wrong were, in fact, accurate. While polls are not always accurate, the reasons we can and can't (rightly) call them wrong are explained in this book.This book will equip readers with the tools to navigate the mismatch of expectations. It is not intended to replace more technical applications of statistics but is accessible to anyone interested in learning more about how poll data should be understood, compared to how it's currently misunderstood.

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Book Synopsismlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. Applied Machine Learning Using mlr3 in R gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R. It covers various key topics, including basic machine learning tasks, such as building and evaluating a predictive model; hyperparameter tuning of machine learning approaches to obtain peak performance; building machine learning pipelines that perform complex operations such as pre-processing followed by modelling followed by aggregation of predictions; and extending the mlr3 ecosystem with custom learners, measures, or pipeline components.Features: In-depth coverage of the mlr3 ecosystem for users and developers Explanation and illustration of basic and advanced machine learning concepts Ready to use code samples that can be adapted by the useTable of Contents1. Introduction and Overview. 2. Data and Basic Modeling. 3. Evaluation and Benchmarking. 4. Hyperparameter Optimization. 5. Advanced Tuning Methods and Black Box Optimization. 6. Feature Selection. 7. Sequential Pipelines. 8. Non-sequential Pipelines and Tuning. 9. Preprocessing. 10. Advanced Technical Aspects of mlr3 .11. Model Interpretation and Explanation. 12. Model Interpretation. 13. Beyond Regression and Classification. 14. Algorithmic Fairness.

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Book SynopsisAn Introduction to Applied Statistics offers a comprehensive and accessible foundation in applied statistics, empowering students with the essential concepts and practical skills necessary for data-driven decision-making in today's world. Thoroughly covering key topicsâincluding data management, probability fundamentals, data screening, descriptive statistics, and a broad spectrum of inferential analysis techniquesâthis indispensable guide demystifies statistical concepts and equips students to confidently apply statistical analysis in real-world contexts.With a systematic, beginner-friendly approach, the author assumes no prior knowledge, making complex statistical foundations accessible to students from a variety of disciplines. Concise, digestible chapters build statistical competencies within a practical, evidence-based framework, minimizing technical jargon to facilitate comprehension. Now in its latest edition, the book is fully updated with SPSS v29.0 instructio

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Book SynopsisThis classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and natural extensions, and consequences, of previous concepts. It covers all topics from a standard inference course including: distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation.Features The classic graduate-level textbook on statistical inference Develops elements of statistical theory from first principles of probability Written in a lucid style accessible to anyone with some background in calculus Covers all key topics of a standard course in inference Hundreds of examples throughout to aid understanding Each chapter includes an extensive set of graduated exercises

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Book SynopsisQuantitative Social Science Research in Practice: Generating Novel and Parsimonious Explanatory Models for Social Sciences examines quantitative Behavioral Science Research (BSR) by focusing on four key areas:Developing Novel, Parsimonious, and Actionable Causal Models: Researchers often face challenges in creating new, parsimonious causal models supported by empirical evaluation. A promising approach involves using meta-analytic reviews and more recent studies to identify relevant constructs and hypotheses that would constitutethe new causal model.Exploring the Scope of Context for a Novel Causal Model: The relevance of causal models may vary based on context, such as national or organizational culture, economic and political situations, and feasibility constraints. Behavioural science researchers have struggled to balance rigor and relevance, as theories effective in one context may not be valid in another. This book presents an approa

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Book SynopsisStatistical Inference via Data Science: A ModernDive into R and the Tidyverse, Second Edition offers a comprehensive guide to learning statistical inference with data science tools widely used in industry, academia, and government. The first part of this book introduces the tidyverse suite of R packages, including ggplot2 for data visualization and dplyr for data wrangling. The second part introduces data modeling via simple and multiple linear regression. The third part presents statistical inference using simulation-based methods within a general framework implemented in R via the infer package, a suitable complement to the tidyverse. By working with these methods, readers can implement effective exploratory data analyses, conduct statistical modeling with data, and carry out statistical inference via confidence intervals and hypothesis testing. All these tasks are performed strongly emphasizing data visualization.Key Features in

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Book SynopsisFinancial Data Analysis with R: Monte-Carlo Validation is a comprehensive exploration of statistical methodologies and their applications in finance. Readers are taken on a journey in each chapter through practical explanations and examples, enabling them to develop a solid foundation of these methods in R and their applications in finance.This book serves as an indispensable resource for finance professionals, analysts, and enthusiasts seeking to harness the power of data-driven decision-making.The book goes beyond just teaching statistical methods in R and incorporates a unique section of informative Monte-Carlo simulations. These Monte-Carlo simulations are uniquely designed to showcase the reader the potential consequences and misleading conclusions that can arise when fundamental model assumptions are violated. Through step-by-step tutorials and realworld cases, readers will learn how and why model assumptions are important to follow.With a focus on

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Book SynopsisThis book offers the theory of causal analysis and its applications. The authors have developed this book in relation its applications to four climatological phenomena, to prove the theory of causal analysis in time sequential data analysis.Local Causal Test and the Partial Causal Test are used to study the theory of causal analysis. These are then applied to understand the climate effects of the eruption at Mt. Pinatubo, effect of the El Nino and Southern Oscillation (ENSO), and North Atlantic Oscillation (NAO). The reader learns about Test(S), a statistical test used to determine if the statistical properties of the data, such as mean and variance, remain constant over time within a local window. The authors also use the Stationary Test and Causality Test in time series to explore relationships within a localized subset of data as witnessed from the climate phenomenon. The book looks at the eruption at Mt. Pinatubo, ENSO and NAO and applies causal theory to study the result

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Book SynopsisFourier analysis aims to decompose functions into a superposition of simple trigonometric functions, whose special features can be exploited to isolate specific components into manageable clusters before reassembling the pieces. This two-volume text presents a largely self-contained treatment, comprising not just the major theoretical aspects (Part I) but also exploring links to other areas of mathematics and applications to science and technology (Part II). Following the historical and conceptual genesis, this book (Part I) provides overviews of basic measure theory and functional analysis, with added insight into complex analysis and the theory of distributions. The material is intended for both beginning and advanced graduate students with a thorough knowledge of advanced calculus and linear algebra. Historical notes are provided and topics are illustrated at every stage by examples and exercises, with separate hints and solutions, thus making the exposition useful both as a course Trade Review'[Fourier Analysis: Volume l - Theory is] fabulous … Constantin structures his exercise sets beautifully, I think: they are abundant and long, covering a spectrum of levels of difficulty; each set is followed immediately by a section of hints (in one-one correspondence); finally the hints sections are followed by very detailed and well-written solutions (also bijectively). Can there be any clearer homage to the maxim that to learn mathematics one has to get one's hands really dirty? To boot, attention to detail is ubiquitous: it's everywhere in Constantin's presentation of proofs and arguments, as well as examples, all throughout the narrative itself. The entire presentation is very much to the point and the student who works through this book will come out knowing some real mathematics very well.' Michael Berg, MAA ReviewsTable of Contents1. Introduction; 2. The Lebesgue measure and integral; 3. Elements of functional analysis; 4. Convergence results for Fourier series; 5. Fourier transforms; 6. Multi-dimensional Fourier analysis; 7. A glance at some advanced topics; Appendix: historical notes; References; Index.

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Book SynopsisStatistics has developed in parallel with the advances of technological and social change. Informed by the work of the Cambridge Mathematics team, this book outlines a new pedagogical approach to teaching statistics. It frames the interconnectedness of the subject around the experiences that students should have, rather than the specific techniques required. The book provides numerous examples and suggestions that teachers can incorporate in the classroom to help improve the way students understand statistics.Table of ContentsIntroduction; Part 1 A vision for statistics in schools: 1. What should all students understand and why is it important?; 2. The statistical cycle; 3. Exploratory data analysis; 4. Simulation; 5. Sampling and variation; 6. Signals and noise; 7. Informal inference; 8. Technology in the classroom; Part 2 Activities; 9. Activity for Chapter 2; 10. Activity for Chapter 3; 11. Activities for Chapter 4; 12. Activities for Chapter 5; 13. Activity for Chapter 6; 14. Activity for Chapter 7; Appendix 1; Appendix 2; Index.

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Book SynopsisStable Lévy processes lie at the intersection of Lévy processes and self-similar Markov processes. Processes in the latter class enjoy a Lamperti-type representation as the space-time path transformation of so-called Markov additive processes (MAPs). This completely new mathematical treatment takes advantage of the fact that the underlying MAP for stable processes can be explicitly described in one dimension and semi-explicitly described in higher dimensions, and uses this approach to catalogue a large number of explicit results describing the path fluctuations of stable Lévy processes in one and higher dimensions. Written for graduate students and researchers in the field, this book systemically establishes many classical results as well as presenting many recent results appearing in the last decade, including previously unpublished material. Topics explored include first hitting laws for a variety of sets, path conditionings, law-preserving path transformations, the distribution of eTrade Review'This treatise takes readers on a superb journey through the fascinating worlds of stable Lévy processes and of a rich variety of further naturally related random processes. Andreas Kyprianou and Juan Carlos Pardo masterfully deploy an arsenal of techniques, which are already interesting on their own right, to reveal many classical or more recent high level results on the distributions of functionals and on the path behaviours stable processes. It is indeed remarkable that their methods lead to so many explicit formulas, some amazingly simple, some more complex. The authors should be praised for making accessible as a coherent whole a vast literature that has been developed over several decades, including the latest developments.' Jean Bertoin, University of ZurichTable of Contents1. Stable distributions; 2. Lévy processes; 3. Stable processes; 4. Hypergeometric Lévy processes; 5. Positive self-similar Markov processes; 6. Spatial fluctuations in one dimension; 7. Doney–Kuznetsov factorisation and the maximum; 8. Asymptotic behaviour for stable processes; 9. Envelopes of positive self-similar Markov processes; 10. Asymptotic behaviour for path transformations; 11. Markov additive and self-similar Markov processes; 12. Stable processes as self-similar Markov processes; 13. Radial reflection and the deep factorisation; 14. Spatial fluctuations and the unit sphere; 15. Applications of radial excursion theory; 16. Windings and up-crossings of stable processes; Appendix.

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Book SynopsisThis book explains all aspects of mixed model analysis without mathematical jargon, so that non-statisticians can understand the basic principles, analyze their own data, and interpret the results with confidence. Worked examples are analyzed with STATA, and all datasets are available for download, equipping readers to replicate the methods.Table of Contents1. Introduction; 2. Basic principles of mixed model analysis; 3. What is gained by using mixed model analysis?; 4. Logistic mixed model analysis; 5. Mixed model analysis with other outcomes; 6. Explaining differences between groups; 7. Multivariable modelling; 8. Predictions based on mixed model analysis; 9. Mixed model analysis for longitudinal data; 10. Multivariate mixed model analysis; 11. Sample size calculations; 12. Some loose ends.

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Book SynopsisThe second edition of Statistics for the Social Sciences prepares students from a wide range of disciplines to interpret and learn the statistical methods critical to their field of study. By using the General Linear Model (GLM), the author builds a foundation that enables students to see how statistical methods are interrelated enabling them to build on the basic skills. The author makes statistics relevant to students'' varying majors by using fascinating real-life examples from the social sciences. Students who use this edition will benefit from clear explanations, warnings against common erroneous beliefs about statistics, and the latest developments in the philosophy, reporting, and practice of statistics in the social sciences. The textbook is packed with helpful pedagogical features including learning goals, guided practice, and reflection questions.Trade Review'Dr Warne's gift for teaching statistics is apparent in his writing of this book. Indeed, I wish I had this book when I was a student. His use of the General Linear Model as a schema for understanding how statistical methods are interrelated sets the book apart from others.' Leena J. Landmark, Associate Professor of Special Education, Sam Houston State University, USATable of Contents1. Statistics and Models; 2. Levels of Data; 3. Models of Central Tendency and Variability; 4. Visual Models; 5. Linear Transformations and z-Scores; 6. Probability and the Central Limit Theorem; 7. Null Hypothesis Statistical Significance Testing and z-Tests; 8. One-Sample t-Tests; 9. Paired-Samples t-Tests; 10. Unpaired Two-Sample t-Tests; 11. Analysis of Variance; 12. Correlation; 13. Regression; 14. Chi-Squared Test; 15 Applying Statistics to Research, and Advanced Statistical Methods.

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Book SynopsisAs a result of the COVID-19 pandemic, medical statistics and public health data have become staples of newsfeeds worldwide, with infection rates, deaths, case fatality and the mysterious R figure featuring regularly. However, we don''t all have the statistical background needed to translate this information into knowledge. In this lively account, Stephen Senn explains these statistical phenomena and demonstrates how statistics is essential to making rational decisions about medical care. The second edition has been thoroughly updated to cover developments of the last two decades and includes a new chapter on medical statistical challenges of COVID-19, along with additional material on infectious disease modelling and representation of women in clinical trials. Senn entertains with anecdotes, puzzles and paradoxes, while tackling big themes including: clinical trials and the development of medicines, life tables, vaccines and their risks or lack of them, smoking and lung cancer, and even the power of prayer.Trade Review'The COVID pandemic has shown the power of statistics to save millions of lives by revealing 'what works'. Yet statistical methods have a deeply controversial history, and provoke sometimes bitter debate to this day. Professor Stephen Senn is renowned for his brilliant insights on the subject, and in Dicing with Death he offers us a series of fascinating journeys through its vast and varied landscape.' Robert Matthews, Visiting Professor Aston University and author of Chancing It: The Laws of Chance and How They Can Work for YouTable of Contents1. Circling the square; 2. The diceman cometh; 3. Trials of life; 4. Of dice and men; 5. Sex and the single patient; 6. A hale view of pills (and other matters); 7. Time's tables; 8. A dip in the pool; 9. The things that bug us; 10. The law is a ass; 11. The empire of the sum; 12. Going viral; Notes; Index.

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Book SynopsisA comprehensive introduction to statistical methods for data mining and knowledge discovery.Table of ContentsPreface xiii Part I Background: Introductory Statistical Analytics 1 1 Data analytics and data mining 3 1.1 Knowledge discovery: finding structure in data 3 1.2 Data quality versus data quantity 5 1.3 Statistical modeling versus statistical description 7 2 Basic probability and statistical distributions 10 2.1 Concepts in probability 10 2.1.1 Probability rules 11 2.1.2 Random variables and probability functions 12 2.1.3 Means, variances, and expected values 17 2.1.4 Median, quartiles, and quantiles 18 2.1.5 Bivariate expected values, covariance, and correlation 20 2.2 Multiple random variables∗ 21 2.3 Univariate families of distributions 23 2.3.1 Binomial distribution 23 2.3.2 Poisson distribution 26 2.3.3 Geometric distribution 27 2.3.4 Negative binomial distribution 27 2.3.5 Discrete uniform distribution 28 2.3.6 Continuous uniform distribution 29 2.3.7 Exponential distribution 29 2.3.8 Gamma and chi-square distributions 30 2.3.9 Normal (Gaussian) distribution 32 2.3.10 Distributions derived from normal 37 2.3.11 The exponential family 41 3 Data manipulation 49 3.1 Random sampling 49 3.2 Data types 51 3.3 Data summarization 52 3.3.1 Means, medians, and central tendency 52 3.3.2 Summarizing variation 56 3.3.3 Summarizing (bivariate) correlation 59 3.4 Data diagnostics and data transformation 60 3.4.1 Outlier analysis 60 3.4.2 Entropy∗ 62 3.4.3 Data transformation 64 3.5 Simple smoothing techniques 65 3.5.1 Binning 66 3.5.2 Moving averages∗ 67 3.5.3 Exponential smoothing∗ 69 4 Data visualization and statistical graphics 76 4.1 Univariate visualization 77 4.1.1 Strip charts and dot plots 77 4.1.2 Boxplots 79 4.1.3 Stem-and-leaf plots 81 4.1.4 Histograms and density estimators 83 4.1.5 Quantile plots 87 4.2 Bivariate and multivariate visualization 89 4.2.1 Pie charts and bar charts 90 4.2.2 Multiple boxplots and QQ plots 95 4.2.3 Scatterplots and bubble plots 98 4.2.4 Heatmaps 102 4.2.5 Time series plots∗ 105 5 Statistical inference 115 5.1 Parameters and likelihood 115 5.2 Point estimation 117 5.2.1 Bias 118 5.2.2 The method of moments 118 5.2.3 Least squares/weighted least squares 119 5.2.4 Maximum likelihood∗ 120 5.3 Interval estimation 123 5.3.1 Confidence intervals 123 5.3.2 Single-sample intervals for normal (Gaussian) parameters 124 5.3.3 Two-sample intervals for normal (Gaussian) parameters 128 5.3.4 Wald intervals and likelihood intervals∗ 131 5.3.5 Delta method intervals∗ 135 5.3.6 Bootstrap intervals∗ 137 5.4 Testing hypotheses 138 5.4.1 Single-sample tests for normal (Gaussian) parameters 140 5.4.2 Two-sample tests for normal (Gaussian) parameters 142 5.4.3 Walds tests, likelihood ratio tests, and ‘exact’ tests∗ 145 5.5 Multiple inferences∗ 148 5.5.1 Bonferroni multiplicity adjustment 149 5.5.2 False discovery rate 151 Part II Statistical Learning and Data Analytics 161 6 Techniques for supervised learning: simple linear regression 163 6.1 What is “supervised learning?” 163 6.2 Simple linear regression 164 6.2.1 The simple linear model 164 6.2.2 Multiple inferences and simultaneous confidence bands 171 6.3 Regression diagnostics 175 6.4 Weighted least squares (WLS) regression 184 6.5 Correlation analysis 187 6.5.1 The correlation coefficient 187 6.5.2 Rank correlation 190 7 Techniques for supervised learning: multiple linear regression 198 7.1 Multiple linear regression 198 7.1.1 Matrix formulation 199 7.1.2 Weighted least squares for the MLR model 200 7.1.3 Inferences under the MLR model 201 7.1.4 Multicollinearity 208 7.2 Polynomial regression 210 7.3 Feature selection 211 7.3.1 R2p plots 212 7.3.2 Information criteria: AIC and BIC 215 7.3.3 Automated variable selection 216 7.4 Alternative regression methods∗ 223 7.4.1 Loess 224 7.4.2 Regularization: ridge regression 230 7.4.3 Regularization and variable selection: the Lasso 238 7.5 Qualitative predictors: ANOVA models 242 8 Supervised learning: generalized linear models 258 8.1 Extending the linear regression model 258 8.1.1 Nonnormal data and the exponential family 258 8.1.2 Link functions 259 8.2 Technical details for GLiMs∗ 259 8.2.1 Estimation 260 8.2.2 The deviance function 261 8.2.3 Residuals 262 8.2.4 Inference and model assessment 264 8.3 Selected forms of GLiMs 265 8.3.1 Logistic regression and binary-data GLiMs 265 8.3.2 Trend testing with proportion data 271 8.3.3 Contingency tables and log-linear models 273 8.3.4 Gamma regression models 281 9 Supervised learning: classification 291 9.1 Binary classification via logistic regression 292 9.1.1 Logistic discriminants 292 9.1.2 Discriminant rule accuracy 296 9.1.3 ROC curves 297 9.2 Linear discriminant analysis (LDA) 297 9.2.1 Linear discriminant functions 297 9.2.2 Bayes discriminant/classification rules 302 9.2.3 Bayesian classification with normal data 303 9.2.4 Naïve Bayes classifiers 308 9.3 k-Nearest neighbor classifiers 308 9.4 Tree-based methods 312 9.4.1 Classification trees 312 9.4.2 Pruning 314 9.4.3 Boosting 321 9.4.4 Regression trees 321 9.5 Support vector machines∗ 322 9.5.1 Separable data 322 9.5.2 Nonseparable data 325 9.5.3 Kernel transformations 326 10 Techniques for unsupervised learning: dimension reduction 341 10.1 Unsupervised versus supervised learning 341 10.2 Principal component analysis 342 10.2.1 Principal components 342 10.2.2 Implementing a PCA 344 10.3 Exploratory factor analysis 351 10.3.1 The factor analytic model 351 10.3.2 Principal factor estimation 353 10.3.3 Maximum likelihood estimation 354 10.3.4 Selecting the number of factors 355 10.3.5 Factor rotation 356 10.3.6 Implementing an EFA 357 10.4 Canonical correlation analysis∗ 361 11 Techniques for unsupervised learning: clustering and association 373 11.1 Cluster analysis 373 11.1.1 Hierarchical clustering 376 11.1.2 Partitioned clustering 384 11.2 Association rules/market basket analysis 395 11.2.1 Association rules for binary observations 396 11.2.2 Measures of rule quality 397 11.2.3 The Apriori algorithm 398 11.2.4 Statistical measures of association quality 402 A Matrix manipulation 411 A.1 Vectors and matrices 411 A.2 Matrix algebra 412 A.3 Matrix inversion 414 A.4 Quadratic forms 415 A.5 Eigenvalues and eigenvectors 415 A.6 Matrix factorizations 416 A.6.1 QR decomposition 417 A.6.2 Spectral decomposition 417 A.6.3 Matrix square root 417 A.6.4 Singular value decomposition 418 A.7 Statistics via matrix operations 419 B Brief introduction to R 421 B.1 Data entry and manipulation 422 B.2 A turbo-charged calculator 426 B.3 R functions 427 B.3.1 Inbuilt R functions 427 B.3.2 Flow control 429 B.3.3 User-defined functions 429 B.4 R packages 430 References 432 Index 453

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Book Synopsis...I know of no better book of its kind... (Journal of the Royal Statistical Society, Vol 169 (1), January 2006) A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to awide range of disciplines. Step-by-step instructionshelp the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material like t--tests and chi--squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling. Includes numerous worked examples and exercises within each chapter.Table of ContentsPreface xi Chapter 1 Fundamentals 1 Everything Varies 2 Significance 3 Good and Bad Hypotheses 3 Null Hypotheses 3 p Values 3 Interpretation 4 Model Choice 4 Statistical Modelling 5 Maximum Likelihood 6 Experimental Design 7 The Principle of Parsimony (Occam’s Razor) 8 Observation, Theory and Experiment 8 Controls 8 Replication: It’s the ns that Justify the Means 8 How Many Replicates? 9 Power 9 Randomization 10 Strong Inference 14 Weak Inference 14 How Long to Go On? 14 Pseudoreplication 15 Initial Conditions 16 Orthogonal Designs and Non-Orthogonal Observational Data 16 Aliasing 16 Multiple Comparisons 17 Summary of Statistical Models in R 18 Organizing Your Work 19 Housekeeping within R 20 References 22 Further Reading 22 Chapter 2 Dataframes 23 Selecting Parts of a Dataframe: Subscripts 26 Sorting 27 Summarizing the Content of Dataframes 29 Summarizing by Explanatory Variables 30 First Things First: Get to Know Your Data 31 Relationships 34 Looking for Interactions between Continuous Variables 36 Graphics to Help with Multiple Regression 39 Interactions Involving Categorical Variables 39 Further Reading 41 Chapter 3 Central Tendency 42 Further Reading 49 Chapter 4 Variance 50 Degrees of Freedom 53 Variance 53 Variance: A Worked Example 55 Variance and Sample Size 58 Using Variance 59 A Measure of Unreliability 60 Confidence Intervals 61 Bootstrap 62 Non-constant Variance: Heteroscedasticity 65 Further Reading 65 Chapter 5 Single Samples 66 Data Summary in the One-Sample Case 66 The Normal Distribution 70 Calculations Using z of the Normal Distribution 76 Plots for Testing Normality of Single Samples 79 Inference in the One-Sample Case 81 Bootstrap in Hypothesis Testing with Single Samples 81 Student’s t Distribution 82 Higher-Order Moments of a Distribution 83 Skew 84 Kurtosis 86 Reference 87 Further Reading 87 Chapter 6 Two Samples 88 Comparing Two Variances 88 Comparing Two Means 90 Student’s t Test 91 Wilcoxon Rank-Sum Test 95 Tests on Paired Samples 97 The Binomial Test 98 Binomial Tests to Compare Two Proportions 100 Chi-Squared Contingency Tables 100 Fisher’s Exact Test 105 Correlation and Covariance 108 Correlation and the Variance of Differences between Variables 110 Scale-Dependent Correlations 112 Reference 113 Further Reading 113 Chapter 7 Regression 114 Linear Regression 116 Linear Regression in R 117 Calculations Involved in Linear Regression 122 Partitioning Sums of Squares in Regression: SSY = SSR + SSE 125 Measuring the Degree of Fit, r 2 133 Model Checking 134 Transformation 135 Polynomial Regression 140 Non-Linear Regression 142 Generalized Additive Models 146 Influence 148 Further Reading 149 Chapter 8 Analysis of Variance 150 One-Way ANOVA 150 Shortcut Formulas 157 Effect Sizes 159 Plots for Interpreting One-Way ANOVA 162 Factorial Experiments 168 Pseudoreplication: Nested Designs and Split Plots 173 Split-Plot Experiments 174 Random Effects and Nested Designs 176 Fixed or Random Effects? 177 Removing the Pseudoreplication 178 Analysis of Longitudinal Data 178 Derived Variable Analysis 179 Dealing with Pseudoreplication 179 Variance Components Analysis (VCA) 183 References 184 Further Reading 184 Chapter 9 Analysis of Covariance 185 Further Reading 192 Chapter 10 Multiple Regression 193 The Steps Involved in Model Simplification 195 Caveats 196 Order of Deletion 196 Carrying Out a Multiple Regression 197 A Trickier Example 203 Further Reading 211 Chapter 11 Contrasts 212 Contrast Coefficients 213 An Example of Contrasts in R 214 A Priori Contrasts 215 Treatment Contrasts 216 Model Simplification by Stepwise Deletion 218 Contrast Sums of Squares by Hand 222 The Three Kinds of Contrasts Compared 224 Reference 225 Further Reading 225 Chapter 12 Other Response Variables 226 Introduction to Generalized Linear Models 228 The Error Structure 229 The Linear Predictor 229 Fitted Values 230 A General Measure of Variability 230 The Link Function 231 Canonical Link Functions 232 Akaike’s Information Criterion (AIC) as a Measure of the Fit of a Model 233 Further Reading 233 Chapter 13 Count Data 234 A Regression with Poisson Errors 234 Analysis of Deviance with Count Data 237 The Danger of Contingency Tables 244 Analysis of Covariance with Count Data 247 Frequency Distributions 250 Further Reading 255 Chapter 14 Proportion Data 256 Analyses of Data on One and Two Proportions 257 Averages of Proportions 257 Count Data on Proportions 257 Odds 259 Overdispersion and Hypothesis Testing 260 Applications 261 Logistic Regression with Binomial Errors 261 Proportion Data with Categorical Explanatory Variables 264 Analysis of Covariance with Binomial Data 269 Further Reading 272 Chapter 15 Binary Response Variable 273 Incidence Functions 275 ANCOVA with a Binary Response Variable 279 Further Reading 284 Chapter 16 Death and Failure Data 285 Survival Analysis with Censoring 287 Further Reading 290 Appendix Essentials of the R Language 291 R as a Calculator 291 Built-in Functions 292 Numbers with Exponents 294 Modulo and Integer Quotients 294 Assignment 295 Rounding 295 Infinity and Things that Are Not a Number (NaN) 296 Missing Values (NA) 297 Operators 298 Creating a Vector 298 Named Elements within Vectors 299 Vector Functions 299 Summary Information from Vectors by Groups 300 Subscripts and Indices 301 Working with Vectors and Logical Subscripts 301 Addresses within Vectors 304 Trimming Vectors Using Negative Subscripts 304 Logical Arithmetic 305 Repeats 305 Generate Factor Levels 306 Generating Regular Sequences of Numbers 306 Matrices 307 Character Strings 309 Writing Functions in R 310 Arithmetic Mean of a Single Sample 310 Median of a Single Sample 310 Loops and Repeats 311 The ifelse Function 312 Evaluating Functions with apply 312 Testing for Equality 313 Testing and Coercing in R 314 Dates and Times in R 315 Calculations with Dates and Times 319 Understanding the Structure of an R Object Using str 320 Reference 322 Further Reading 322 Index 323

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Book SynopsisThe book has been tested and refined through years of classroom teaching experience. With an abundance of examples, problems, and fully worked out solutions, the text introduces the financial theory and relevant mathematical methods in a mathematically rigorous yet engaging way. This textbook provides complete coverage of continuous-time financial models that form the cornerstones of financial derivative pricing theory. Unlike similar texts in the field, this one presents multiple problem-solving approaches, linking related comprehensive techniques for pricing different types of financial derivatives.Key features: In-depth coverage of continuous-time theory and methodology Numerous, fully worked out examples and exercises in every chapter Mathematically rigorous and consistent, yet bridging various basic and more advanced concepts Judicious balance of financial theory and mathematiTable of ContentsPart I: Stochastic Calculus with Brownian Motion. 1. One-Dimensional Brownian Motion and Related Processes. 2. Introduction to Continuous-Time Stochastic Calculus. Part II Continuous-Time Modelling. 3. Risk-Neutral Pricing in the (B; S) Economy: One Underlying Stock. 4. Risk-Neutral Pricing in a Multi-Asset Economy. 5. American Options. 6. Interest-Rate Modelling and Derivative Pricing. 7. Alternative Models of Asset Price Dynamics. A. Essentials of General Probability Theory. B. Some Useful Integral (Expectation) Identities and Symmetry Properties of Normal Random Variables. C. Answers and Hints to Exercises. D. Glossary of Symbols and Abbreviations. Greek Alphabet. References. Index.

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Book SynopsisThis is the sixth edition of a popular textbook on multivariate analysis. Well-regarded for its practical and accessible approach, with excellent examples and good guidance on computing, the book is particularly popular for teaching outside statistics, i.e. in epidemiology, social science, business, etc. The sixth edition has been updated with a new chapter on data visualization, a distinction made between exploratory and confirmatory analyses and a new section on generalized estimating equations and many new updates throughout. This new edition will enable the book to continue as one of the leading textbooks in the area, particularly for non-statisticians. Key Features: Provides a comprehensive, practical and accessible introduction to multivariate analysis. Keeps mathematical details to a minimum, so particularly geared toward a non-statistical audience. Includes lots of detailed worked examples, guidance on computiTrade Review"This book is an excellent resource for students and researchers of all levels. I have used earlier editions repeatedly in data-analysis courses for advanced undergraduates and graduate students in applied fields. The level of mathematical presentation is well matched to such settings. Not only are there excellent examples from biostatistics and public health, but there are also some very good business financial examples. The new chapter on Data Visualization in the new, sixth edition will be especially useful. Overall, the book is exceptionally well written and readable."- Stanley Sclove, University of Illinois at Chicago "Editions of Practical Multivariate Analysis have been the mainstay of my graduate-level service course in applied data-analysis since 1985. It remains an extraordinary book -- packed with excellent examples, clear explanation and fine advice -- and has my highest possible recommendation. Among many reasons it remains so extraordinary, are three signaled directly in its title: it is practical rather than theoretical, analytic rather than technical, and it embodies a broader-than-usual conception of utilitarian multivariate methods. Practical Multivariate Analysis connects readily to its audience’s reality. It uses concrete research questions and real data to motivate its content, illustrated by exemplary analyses using R, SAS, SPSS and STATA. It models how complex findings can be made comprehensible to a broader community. It reaches beyond the typical spectrum of multivariate methods. It begins sensibly, discussing how multivariate data can be explored and displayed before complex analysis. Then come chapters on useful extensions to multiple regression analysis. While not usually considered “multivariate,” these latter methods connect an incoming audience to earlier acquired skills and extend them. Then follow the core chapters on “standard” multivariate methods, including canonical correlation, discriminant, principal-components, factor and cluster analyses. All are clearly presented, and then extended by excellent chapters on logistic regression, survival and log-linear analyses, and multilevel modeling, techniques that have proven useful and ubiquitous throughout social-science research.In my view, Practical Multivariate Analysis is an excellent roadmap for conducting such analyses, and a fine model for ensuring that their complex findings can be communicated successfully to others."- John B. Willett, Charles William Eliot Research Professor, Harvard University Graduate School of Education "The Practical Multivariate Analysis is a fun statistical modeling book to read. I enjoyed the rich insights the book has provided, which can only be accumulated through years of experience with the complexity in real data. It covers a large collection of statistical methods and models with a clear focus on application. Always discussing a model or method along with data examples, the book helps readers focus on important perspectives in applying the model, from choice of appropriate methods to interpretation of the results, while it still manages to maintain thetechnique details at a minimal level. Readers with different backgrounds can all benefit from this book. It is valuable for researchers who are interested in analyzing their data with classical statistical models and interpreting the results. It is a good reading for new graduates in statistics who have not had a lot of experience with real data as the book provides many importance guidance in handling real data as well as watch-out advices. It can be used by applied data scientists and serve as a resourceful reference book for experienced consultants."- Xia Wang, University of Cincinnati "The monograph belongs to the series Texts in Statistical Science and presents the sixth upgraded edition of the popular manual. It was first issued in 1984, and from that time won recognition as one of the best textbooks on the applied statistical modeling and analysis...Most of chapters of the first part of the textbook contain such subsections as “Introduction” or “Definition,” “Discussion” or “Examples,” “Summary” and “Problems”...This structure makes the book very reader-friendly written, helping to students and researchers in various fields to understand what for a statistical tool can serve, how to apply it, and to interpret computer outputs. There is not much of mathematical and statistical derivation, neither modern statistical techniques, but plenty of examples oriented to the easy “know-how” practical implementations of the classical multivariate methods."- Stan Lipovetsky, Technometrics, Vol 62"The authors wrote the sixth edition of this book for biomedical scientists, behavioural scientists, and academic researchers, who wish to perform and understand the results of multivariate statistical analyses. The book also describes when to ask for help from a statistical expert on multivariate analysis...The sixth edition has been updated with, in particular, a new chapter on data visualization, a distinction made between exploratory and confirmatory analyses, and a new section on generalized estimating equations. This new edition will enable the book to continue as one of the leading textbooks in the area, particularly for non-statisticians, since it provides a comprehensive, practical, and accessible introduction to multivariate analysis whilst keeping mathematical details to a minimum...The book is an excellent roadmap for multivariate analysis and a fine model for ensuring that complex findings can be successfully communicated in a paper."- Luca Bertolaccini, ISCB News, July 2020 "This book is an excellent resource for students and researchers of all levels. I have used earlier editions repeatedly in data-analysis courses for advanced undergraduates and graduate students in applied fields. The level of mathematical presentation is well matched to such settings. Not only are there excellent examples from biostatistics and public health, but there are also some very good business financial examples. The new chapter on Data Visualization in the new, sixth edition will be especially useful. Overall, the book is exceptionally well written and readable."- Stanley Sclove, University of Illinois at Chicago "Editions of Practical Multivariate Analysis have been the mainstay of my graduate-level service course in applied data-analysis since 1985. It remains an extraordinary book -- packed with excellent examples, clear explanation and fine advice -- and has my highest possible recommendation. Among many reasons it remains so extraordinary, are three signaled directly in its title: it is practical rather than theoretical, analytic rather than technical, and it embodies a broader-than-usual conception of utilitarian multivariate methods. Practical Multivariate Analysis connects readily to its audience’s reality. It uses concrete research questions and real data to motivate its content, illustrated by exemplary analyses using R, SAS, SPSS and STATA. It models how complex findings can be made comprehensible to a broader community. It reaches beyond the typical spectrum of multivariate methods. It begins sensibly, discussing how multivariate data can be explored and displayed before complex analysis. Then come chapters on useful extensions to multiple regression analysis. While not usually considered “multivariate,” these latter methods connect an incoming audience to earlier acquired skills and extend them. Then follow the core chapters on “standard” multivariate methods, including canonical correlation, discriminant, principal-components, factor and cluster analyses. All are clearly presented, and then extended by excellent chapters on logistic regression, survival and log-linear analyses, and multilevel modeling, techniques that have proven useful and ubiquitous throughout social-science research.In my view, Practical Multivariate Analysis is an excellent roadmap for conducting such analyses, and a fine model for ensuring that their complex findings can be communicated successfully to others."- John B. Willett, Charles William Eliot Research Professor, Harvard University Graduate School of Education "The Practical Multivariate Analysis is a fun statistical modeling book to read. I enjoyed the richinsights the book has provided, which can only be accumulated through years of experience withthe complexity in real data. It covers a large collection of statistical methods and models with aclear focus on application. Always discussing a model or method along with data examples, thebook helps readers focus on important perspectives in applying the model, from choice ofappropriate methods to interpretation of the results, while it still manages to maintain thetechnique details at a minimal level.Readers with different backgrounds can all benefit from this book. It is valuable for researcherswho are interested in analyzing their data with classical statistical models and interpreting theresults. It is a good reading for new graduates in statistics who have not had a lot of experiencewith real data as the book provides many importance guidance in handling real data as well aswatch-out advices. It can be used by applied data scientists and serve as a resourceful referencebook for experienced consultants."- Xia Wang, University of Cincinnati "The monograph belongs to the series Texts in Statistical Science and presents the sixth upgraded edition of the popular manual. It was first issued in 1984, and from that time won recognition as one of the best textbooks on the applied statistical modeling and analysis...Most of chapters of the first part of the textbook contain such subsections as “Introduction” or “Definition,” “Discussion” or “Examples,” “Summary” and “Problems”...This structure makes the book very reader-friendly written, helping to students and researchers in various fields to understand what for a statistical tool can serve, how to apply it, and to interpret computer outputs. There is not much of mathematical and statistical derivation, neither modern statistical techniques, but plenty of examples oriented to the easy “know-how” practical implementations of the classical multivariate methods."- Stan Lipovetsky, Technometrics, Vol 62 Table of ContentsPart I: Preparation for Analysis. What is Multivariate Analysis? Characterizing Data for Analysis. Preparing for Data Analysis. Data Visualization. Data Screening and Transformations. Data Visualization. Selecting Appropriate Analyses. Part II: Regression Analysis. Simple Regression and Correlation. Multiple Regression and Correlation. Variable Selection in Regression. Special Regression Topics. Discriminant analysis. Logistic Regression. Regression Analysis with Survival Data. Principal Components Analysis. Factor Analysis. Cluster Analysis. Log-Linear Analysis. Correlated Outcomes Regression.

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Book SynopsisA leading data visualisation expert explores the negativeand positive-influences that charts have on our perception of truth.Trade Review"[Alberto Cairo's] book reminds readers not to infer too much from a chart, especially when it shows them what they already wanted to see. Mr Cairo has sent a copy to the White House." -- The Economist

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Book SynopsisStatistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications.The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, Trade Review"This book delivers a very comprehensive summary of the development of statistical foundations of data science. The authors no doubt are doing frontier research and have made several crucial contributions to the field. Therefore, the book offers a very good account of the most cutting-edge development. The book is suitable for both master and Ph.D. students in statistics, and also for researchers in both applied and theoretical data science. Researchers can take this book as an index of topics, as it summarizes in brief many significant research articles in an accessible way. Each chapter can be read independently by experienced researchers. It provides a nice cover of key concepts in those topics and researchers can benefit from reading the specific chapters and paragraphs to get a big picture rather than diving into many technical articles. There are altogether 14 chapters. It can serve as a textbook for two semesters. The book also provides handy codes and data sets, which is a great treasure for practitioners."~Journal of Time Series Analysis"This text—collaboratively authored by renowned statisticians Fan (Princeton Univ.), Li (Pennsylvania State Univ.), Zhang (Rutgers Univ.), and Zhou (Univ. of Minnesota)—laboriously compiles and explains theoretical and methodological achievements in data science and big data analytics. Amid today's flood of coding-based cookbooks for data science, this book is a rare monograph addressing recent advances in mathematical and statistical principles and the methods behind regularized regression, analysis of high-dimensional data, and machine learning. The pinnacle achievement of the book is its comprehensive exploration of sparsity for model selection in statistical regression, considering models such as generalized linear regression, penalized least squares, quantile and robust regression, and survival regression. The authors discuss sparsity not only in terms of various types of penalties but also as an important feature of numerical optimization algorithms, now used in manifold applications including deep learning. The text extensively probes contemporary high-dimensional data modeling methods such as feature screening, covariate regularization, graphical modeling, and principal component and factor analysis. The authors conclude by introducing contemporary statistical machine learning, spanning a range of topics in supervised and unsupervised learning techniques and deep learning. This book is a must-have bookshelf item for those with a thirst for learning about the theoretical rigor of data science."~Choice Review, S-T. Kim, North Carolina A&T State University, August 2021Table of Contents1. Introduction. 2. Multiple and Nonparametric Regression. 3. Introduction to Penalized Least-Squares. 4. Penalized Least Squares: Properties. 5. Generalized Linear Models and Penalized Likelihood. 6. Penalized M-estimators. 7. High Dimensional Inference 8. Feature Screening. 9. Covariance Regularization and Graphical Models. 10. Covariance Learning and Factor Models. 11. Applications of Factor Models and PCA. 12. Supervised Learning. 13. Unsupervised Learning. 14. An Introduction to Deep Learning.

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Book SynopsisRisk Analysis in Engineering and Economics is required reading for decision making under conditions of uncertainty. The author describes the fundamental concepts, techniques, and applications of the subject in a style tailored to meet the needs of students and practitioners of engineering, science, economics, and finance. Drawing on his extensive experience in uncertainty and risk modeling and analysis, the author covers everything from basic theory and key computational algorithms to data needs, sources, and collection. He emphasizes practical use of the methods presented and carefully examines the limitations, advantages, and disadvantages of each to help readers translate the discussed techniques into real-world solutions.This Second Edition: Introduces the topic of risk finance Incorporates homeland security applications throughout Offers additional material on predictive risk management Includes a wealth of neTrade ReviewPraise for the First Edition "This book ambitiously tackles risk analysis from the ground up. … an excellent reference for individuals studying in a variety of risk-related disciplines. … enlightening [examples]…attractive to students approaching risk studies from a variety of substantive backgrounds. … Advanced students are likely to find Ayyub's treatments…exceedingly valuable."—Journal of the American Statistical Association, June 2004, Vol. 99, No. 466 "… a good textbook and reference. The mix of engineering and economics is well balanced. … Valuable for students after college and useful for professional engineers."—Technometrics, August 2004, Vol. 46, No. 3 Praise for the Second Edition "… one of a kind in the market. … I am not aware of books that cover the subject in as comprehensive a manner."—Professor Nii O. Attoh-Okine, University of Delaware, Newark, USA Table of ContentsIntroduction. Risk Analysis Methods. System Definition and Structure. Reliability Assessment. Failure Consequences and Severity. Engineering Economics and Finance. Risk Control Methods. Data for Risk Studies. Fundamentals of Probability and Statistics. Failure Data. References and Bibliography. Index.

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Book SynopsisNew Tools to Solve Your Option Pricing ProblemsFor nonlinear PDEs encountered in quantitative finance, advanced probabilistic methods are needed to address dimensionality issues. Written by two leaders in quantitative researchincluding Risk magazine's 2013 Quant of the YearNonlinear Option Pricing compares various numerical methods for solving high-dimensional nonlinear problems arising in option pricing. Designed for practitioners, it is the first authored book to discuss nonlinear Black-Scholes PDEs and compare the efficiency of many different methods. Real-World Solutions for Quantitative Analysts The book helps quants develop both their analytical and numerical expertise. It focuses on general mathematical tools rather than specific financial questions so that readers can easily use the tools to solve their own nonlinear problems. The authors build intuition through numerous real-world examples of numTrade Review"... provides a wide overview of the advanced modern techniques applied in financial modeling. It gives an optimal combination of analytical and numerical tools in quantitative finance. It could provide guidance on the development of nonlinear methods of option pricing for practitioners as well as for analysts."—Nikita Y. Ratanov, from Mathematical Reviews Clippings, January 2015"… anyone with interest in quantitative finance and partial differential equations/continuous time stochastic analysis will not only greatly enjoy this book, but he or she will find both many numerical ideas of real practical interest as well as material for academic research, perhaps for years to come."—Peter Friz, The Bachelier Finance Society"This textbook provides a comprehensive treatment of numerical methods for nonlinear option pricing problems."—Zentralblatt MATH 1285"It is the only book of its kind. … The contribution of this book is threefold: (a) a practical, intuitive, and self-contained derivation of various of the latest derivative pricing models driven by diffusion processes; (b) an exposition of various advanced Monte Carlo simulation schemes for solving challenging nonlinear problems arising in financial engineering; (c) a clear and accessible survey of the theory of nonlinear PDEs. The authors have done a brilliant job providing just the right amount of rigorous theory required to understand the advanced methodologies they present. … Julien Guyon and Pierre Henry-Labordère, as befitting their reputations as star quants, have done an excellent job presenting the latest theory of nonlinear PDEs and their applications to finance. Much of the material in the book consists of the authors’ own original results. I highly recommend this book to seasoned mathematicians and experienced quants in the industry … Mathematicians will be able to see how practitioners argue heuristically to arrive at solutions of the toughest problems in financial engineering; practitioners of quantitative finance will find the book perfectly balanced between mathematical theory, financial modelling, and schemes for numerical implementation."—Quantitative Finance, 2014"Ever since Black and Scholes solved their eponymous linear PDE in 1969, the complexity of problems plaguing financial practitioners has exploded (non-linearly!). How fitting it is that nonlinear PDEs are now routinely used to extend the original framework. Written by two leading quants at two leading financial houses, this book is a tour de force on the use of nonlinear PDEs in financial valuation."—Peter Carr, PhD, Global Head of Market Modeling, Morgan Stanley, New York, and Executive Director of Masters in Mathematical Finance, Courant Institute of Mathematical Sciences, New York University "Finance used to be simple; you could go a long way with just linearity and positivity but this is not the case anymore. This superb book gives a wide array of modern methods for modern problems."—Bruno Dupire, Head of Quantitative Research, Bloomberg L.P."In this unique and impressive book, the authors apply sophisticated modern tools of pure and applied mathematics, such as BSDEs and particle methods, to solve challenging nonlinear problems of real practical interest, such as the valuation of guaranteed equity-linked annuity contracts and the calibration of local stochastic volatility models. Not only that, but sketches of proofs and implementation details are included. No serious student of mathematical finance, whether practitioner or academic, can afford to be without it."—Jim Gatheral, Presidential Professor, Baruch College, CUNY, and author of The Volatility Surface"Guyon and Henry-Labordère have produced an impressive textbook, which covers options and derivatives pricing from the point of view of nonlinear PDEs. This book is a comprehensive survey of nonlinear techniques, ranging from American options, uncertain volatility, and uncertain correlation models. It is aimed at graduate students or quantitative analysts with a strong mathematical background. They will find the book reasonably self-contained, i.e., discussing both the mathematical theory and the applications, in a very balanced approach. A must-read for the serious quantitative analyst."—Marco Avellaneda, Courant Institute of Mathematical Sciences, New York UniversityTable of ContentsSome Excursions in Option Pricing. Nonlinear PDEs: A Bit of Theory. Examples of Nonlinear Problems in Finance. Early Exercise Problems. Backward Stochastic Differential Equations. The Uncertain Lapse and Mortality Model. The Uncertain Volatility Model. McKean Nonlinear Stochastic Differential Equations. Calibration of Local Stochastic Volatility Models to Market Smiles. Calibration of Local Correlation Models to Market Smiles. Marked Branching Diffusions. References. Index.

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Book SynopsisLike the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.New to the Third Edition Three new chapters on quantile regression, missing values, and Bayesian inference Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables Additional exercises More detailed explanations of R code New section in each chapter summarizing the results of the analyses Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses Whether you're a data analyst, scientist, or student, this handTrade Review“I truly appreciate how grounded in practicality this book is—and the way its chapters are structured really underlines this. Furthermore, all the datasets are interesting and vary widely in subject matter. If nothing else, this book is an excellent source of examples one might use to illustrate a variety of statistical techniques. … it offers a lot of good places to start if one wants to analyze data. … The book comes hand-in-hand with an R package, HSAUR3, with all the data and the code used in the text. The book is thus fully reproducible. Overall, it provides a great way for a statistician to get started doing a wide variety of things in the R environment. It would be particularly useful, then, for working statisticians looking to change their software. The book cites all the relevant packages one might need, which is quite nice for those attempting to navigate the vast array of packages freely available, and is quite clear in its presentation of the code. Between this and the datasets, it makes for quite a valuable and enjoyable reference.”—The American Statistician, August 2015"… a handy primer for using R to perform standard statistical data analysis. … students, analysts, professors, and scientists: if you are looking to add R to your toolkit for analyzing data statistically, then this book will get you there."—Kendall Giles on his blog, September 2014Praise for the Second Edition:"I find the book by Everitt and Hothorn quite pleasant and bound to fit its purpose. The layout and presentation [are] nice. It should appeal to all readers as it contains a wealth of information about the use of R for statistical analysis. Included seasoned R users: When reading the first chapters, I found myself scribbling small lightbulbs in the margin to point out features of R I was not aware of. In addition, the book is quite handy for a crash introduction to statistics for (well-enough motivated) nonstatisticians."—International Statistical Review (2011), 79"… an extensive selection of real data analyzed with [R] … Viewed as a collection of worked examples, this book has much to recommend it. Each chapter addresses a specific technique. … the examples provide a wide variety of partial analyses and the datasets cover a diversity of fields of study. … This handbook is unusually free of the sort of errors spell checkers do not find."—MAA Reviews, April 2011Table of ContentsAn Introduction to R. Data Analysis Using Graphical Displays. Simple Inference. Conditional Inference. Analysis of Variance. Simple and Multiple Linear Regression. Logistic Regression and Generalized Linear Models. Density Estimation. Recursive Partitioning. Scatterplot Smoothers and Additive Models. Survival Analysis. Quantile Regression. Analyzing Longitudinal Data I. Analyzing Longitudinal Data II. Simultaneous Inference and Multiple Comparisons. Missing Values. Meta-Analysis. Bayesian Inference. Principal Component Analysis. Multidimensional Scaling. Cluster Analysis. Bibliography. Index.

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Book SynopsisThis book presents a unified and up-to-date introduction to ROC methodologies, covering both diagnosis (classification) and prediction. The emphasis is on the conceptual underpinning of ROC analysis and the practical implementation in diverse scientific fields. A plethora of examples accompany the methodologic discussion using standard statistical software such as R and STATA. The book arrives after two decades of intensive growth in both the methods and the applications of ROC analysis and presents a new synthesis. The authors provide a contemporary, integrated exposition of ROC methodology for both classification and prediction and include material on multiple-class ROC. This book avoids lengthy technical exposition and provides code and datasets in each chapter. ROC Analysis for Classification and Prediction in Practice is intended for researchers and graduate students, but will also be useful for those that use ROC analysis in diverse disciplines such as diagnostic medicineTable of Contents1. Introduction 2. Measures of Diagnostic and Predictive Performance 3. Statistical inference for the ROC curve 4. Comparing ROC curves 5. The ROC surface and k-class classification for k > 2 6. ROC regression 7. Missing data and errors-in-variables in ROC analysis

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisEmpirical research has now become an essential component of software engineering yet software practitioners and researchers often lack an understanding of how the empirical procedures and practices are applied in the field. Empirical Research in Software Engineering: Concepts, Analysis, and Applications shows how to implement empirical research processes, procedures, and practices in software engineering.Written by a leading researcher in empirical software engineering, the book describes the necessary steps to perform replicated and empirical research. It explains how to plan and design experiments, conduct systematic reviews and case studies, and analyze the results produced by the empirical studies. The book balances empirical research concepts with exercises, examples, and real-life case studies, making it suitable for a course on empirical software engineering. The author discusses the process of developing predictive models, such asTrade Review"In this book, Dr. Malhotra uses her breadth of software engineering experience and expertise to give the reader coverage of many aspects of empirical software engineering. She covers the essential techniques and concepts needed for a researcher to get started on empirical software engineering research, including metrics, experimental design, analysis and statistical techniques, threats to the validity of any research findings, and methods and tools for empirical software engineering research. … The book provides the reader with an introduction and overview of the field and is also backed by references to the literature, allowing the interested reader to follow up on the methods, tools, and concepts described."—From the Foreword by Mark Harman, University College LondonTable of ContentsIntroduction. Systematic Literature Reviews. Software Metrics. Experimental Design. Mining Data from Software Repositories. Data Analysis and Statistical Testing. Model Development and Interpretation. Validity Threats. Reporting Results. Mining Unstructured Data. Demonstrating Empirical Procedures. Tools for Analyzing Data. Appendix. References. Index.

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Book SynopsisMathematical Statistics: Basic Ideas and Selected Topics, Volume II presents important statistical concepts, methods, and tools not covered in the authors' previous volume. This second volume focuses on inference in non- and semiparametric models. It not only reexamines the procedures introduced in the first volume from a more sophisticated point of view but also addresses new problems originating from the analysis of estimation of functions and other complex decision procedures and large-scale data analysis.The book covers asymptotic efficiency in semiparametric models from the Le Cam and Fisherian points of view as well as some finite sample size optimality criteria based on LehmannScheffé theory. It develops the theory of semiparametric maximum likelihood estimation with applications to areas such as survival analysis. It also discusses methods of inference based on sieve models and asymptotic testing theory. The remainder of the book is devoted to Trade Review" . . . the authors have done a superb job of selecting topics comprising most of the essential knowledge needed formodern research. Furthermore, these modern topics are considered with greater depth and sophistication than is usual in a general purpose text. And throughout its pages the book does a good job of linking the mathematical developments to major examples. The choice of topics and examples, along with the depth of coverage are the most attractive features of this volume."~RobertW. Keener, University of MichiganTable of ContentsIntroduction and Examples. Tools for Asymptotic Analysis. Distribution-Free, Unbiased, and Equivariant Procedures. Inference in Semiparametric Models. Monte Carlo Methods. Nonparametric Inference for Functions of One Variable. Prediction and Machine Learning. Appendices. References. Indices.

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Book SynopsisThis book is among the first to present the mathematical models most commonly used to solve optimal execution problems and market making problems in finance. The Financial Mathematics of Market Liquidity: From Optimal Execution to Market Making presents a general modeling framework for optimal execution problemsinspired from the Almgren-Chriss approachand then demonstrates the use of that framework across a wide range of areas.The book introduces the classical tools of optimal execution and market making, along with their practical use. It also demonstrates how the tools used in the optimal execution literature can be used to solve classical and new issues where accounting for liquidity is important. In particular, it presents cutting-edge research on the pricing of block trades, the pricing and hedging of options when liquidity matters, and the management of complex share buy-back contracts.What sets this book apart from others is that it focuses onTrade Review"This excellent monograph covers the mathematical theory of market microstructure with particular emphasis in models of optimal execution and market making. Gueant’s book is a superb introduction to these topics for graduate students in mathematical finance or quants who want to work in execution algorithms or market-making strategies."—Jose A. Scheinkman, Charles and Lynn Zhang Professor of Economics, Columbia University, and Theodore Wells '29 Professor of Economics Emeritus, Princeton University"This is a very timely book that cuts across various fields (applied mathematics, operations research, and quantitative finance). Execution costs due to market illiquidity can significantly reduce returns on investment strategies and, for this reason, affect asset prices. It is therefore important to design trading strategies minimizing these costs and to account for their effect on prices. In the last decade, ‘quants’ and researchers in quantitative finance have made considerable progress on these issues, integrating in their models changes in the way financial markets work (e.g., the development of continuous limit order books, market fragmentation, dark pools, the automation of trading, etc.). "Olivier Guéant’s book takes stock of this effort by providing a rigorous and expert presentation of mathematical tools, models, and numerical methods developed in this area. I strongly recommend it for researchers and graduate students interested in how illiquidity costs affect trading strategies and should be accounted for in asset valuation problems."—Thierry Foucault, HEC Foundation Chair Professor of Finance, HEC, Paris"This book is a must-have for quantitative analysts working at algorithmic trading desks. Olivier Guéant could have written a sophisticated book dedicated to cutting-edge research. He rather decided to put his talent at the service of a far more difficult task: deliver a clear view of modern algorithmic trading to strats or quants having decent scientific training. Scientists will find here all the needed keys to control the intraday risk of their trading models, improving their overall efficiency. Covering brokerage algorithms, market making, hedging, and share buyback techniques, this book is the definitive reference for algorithm builders.Moreover, Olivier links algorithmic trading with market microstructure during the first chapter of the book, including interesting thoughts on corporate bonds trading. On the other hand, he provides a nice introduction to mathematical economics in the Appendix. This book is resolutely more than a bunch of equations thrown on blank pages. I consider it an important step forward in the building of the mathematics of market microstructure."—Charles-Albert Lehalle, Senior Research Advisor, Capital Fund ManagementTable of ContentsIntroduction. Optimal Liquidation. Liquidity in Pricing Models. Market Making.

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Book SynopsisIntroduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework.The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors, as well as for MS and PhD students in other disciplines, including applied mathematics, environmental science, public health, medical research, geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation. Each chapter contains plentiful examples of relevant R code and theoretical and data analytic problems.The material of the book can be roughly divided into four parts of approximaTrade Review"This well-written book provides a great and intuitive introduction to functional data analysis (FDA) which has emerged as an important area in statistics and found tons of scientific applications...This book succeeds at introducing this novel statistical concept and methodology while keeps the level of mathematical and statistical sophistication required to understand at the level of an introductory graduate-level course, which makes for pleasant reading. A nice feature of the book is its strong focus on implementation using R, which makes it a great candidate of textbooks or reference books for (master-level) graduate students and applied researchers...Some unique features of this book as compared to existing ones include (1) its strong focus on implementation using R; (2) chapters on Sparse FDA, generalized functional linear models, functional time series, and spatial functional data; (3) well-designed exercises that can be used as homework problems." ~Xianyang Zhang, Texas A&M University"The main advantage of the book is its emphasis introducing the material through realistic examples and computational tools, while also providing mathematical guidance for the methodologies. Also, important topics like functional time series and spatial functional data are not adequately covered in comparable texts like Ramsay and Silverman, Ramsay and Hooker, Ferraty and Vieu, and Hsing and Eubank. In that respect, the book offers additional and practically relevant material and perspective." ~Debashis Paul, University of California, Davis"The classic tools from the field of functional data analysis are introduced comprehensively and immediately put into a framework of potential application. I would probably advise any reader that is new to functional data analysis to start by reading this book." ~Claudia Klüppelberg, Technische Universität München"Being more advanced and up to date than the Ramsay and Silverman, it complements various topics that are just briefly mentioned or not covered at all by Ramsay and Silverman." ~Laura Sangali, Politecnico di Milano"As a relatively young subfield of statistics, functional data analysis (FDA) has not had a large glut of textbooks pertaining to it. The most famous of the FDA books is the classic text by J. O. Ramsay and B. W. Silverman [Functional data analysis, Springer Ser. Statist., Springer, New York, 1997; second edition, 2005; MR2168993], which introduced many statisticians to the area. Ramsay and Silverman [Applied functional data analysis, Springer Ser. Statist., Springer, New York, 2002; MR1910407] provided a useful collection of FDA case studies, and Ramsay, G. Hooker and S. Graves [Func-tional data analysis with R and MATLAB, Use R, Springer, New York, 2009, doi:10. 1007/978-0-387-98185-7] presented R and MATLAB code for analyzing real functional data sets. [F. Ferraty and P. Vieu, Nonparametric functional data analysis, Springer Ser. Statist., Springer, New York, 2006; MR2229687] and [T. Hsing and R. L. Eubank, Theoretical foundations of functional data analysis, with an introduction to linear opera- tors, Wiley Ser. Probab. Stat., Wiley, Chichester, 2015; MR3379106] are well-respected theoretical presentations of FDA.This book by Kokoszka and Reimherr provides a nice mix of foundational material, accessible theory, and practical examples (including much R code). It is a valuable addition to the FDA literature, and is perhaps an ideal choice of a course textbook for either an undergraduate or graduate course in FDA, whereas several of the other textbooks are more valuable as references for researchers and practitioners than as tutorials for learners. At the end of each chapter is a nice variety of problems that instructors could use for homework assignments.Chapter 1 introduces basic terminology related to FDA, such as the ubiquitous tool of basis expansion and the distinction between dense and sparse functional data. Summary statistics and plots (sample mean and covariance functions, principal components analysis (PCA), functional boxplots) for FDA are briey presented. Chapter 2 continues basic FDA topics with a discussion of derivative information, penalized smoothing, and alignment/registration of curves.The theoretical underpinnings of FDA are presented quickly in Chapter 3, where topics such as square integrable functions, random functions following some distribution, and operator theory are defined briey. A fuller coverage of theoretical concerns is saved for (the optional in a course setting) Chapters 10 and 11. The heart of the book is Chapters 4 through 9, which cover functional linear models in detail, before moving on to specialized FDA topics such as sparse FDA, functionaltime series, and spatial functional data. Scalar-on-function regression, in which the response is a scalar and the predictor is a function, is treated in Chapter 4, and illustrated via the use of the refund package in R. Nonlinear scalar-on-function regression is briey mentioned. Chapter 5 covers both the function-on-scalar regression case and the fully functional regression model in which both response and predictor are functions. Testing and validation of the functional linear model are also shown. Chapter 6 covers functional generalized linear models (GLMs) which have a nonnormal scalar response and a functional predictor. The somewhatnebulous situation with functional-response GLMs is briey covered as well. The next chapter deals with sparse functional data, and presents methods for mean function estimation, covariance function estimation, PCA, and regression in the sparse case when relatively few points are measured for each observed curve. Functional time series occur when the sample functions are observed sequentially over time rather than cross-sectionally. The assumption of independent functional data fails in this case, and Chapter 8 presents a functional autoregressive model for such data that can be used for forecasting. Spatial functional data may commonly be encountered in geostatistics when curves are observed both over time and at various spatial locations. Chapter 9 discusses models for such data and prediction using functional kriging. Chapter 12 discusses treating a functional data set as a sample from some population of functions and performing inference on the population. Of particular interest are the methods presented for formal hypothesis tests and confidence bands about the population mean function.Clustering and classification of functional data are not discussed in detail in thisbook, nor is FDA on manifolds, although references are given to guide readers to recentresearch in these areas."~David Benner HitchcockTable of ContentsFirst steps in the analysis of functional data Basis expansions Sample mean and covariance Principal component functions Analysis of BOA stock returns Diffusion tensor imaging Problems Further topics in exploratory FDA Derivatives Penalized smoothing Curve alignment Further reading Problems Mathematical framework for functional data Square integrable functions Random functions Linear transformations Scalar- on - function regressionExamples Review of standard regression theory Difficulties specific to functional regression Estimation through a basis expansion Estimation with a roughness penalty Regression on functional principal components Implementation in the refund package Nonlinear scalar-on-function regression Problems Functional response models Least squares estimation and application to angular motionPenalized least squares estimation Functional regressors Penalized estimation in the refund package Estimation based on functional principal components Test of no effectVerification of the validity of a functional linear model Extensions and further reading Problems Functional generalized linear models Background Scalar-on-function GLM's Functional response GLM Implementation in the refund package Application to DTI Further reading Problems Sparse FDA Introduction Mean function estimationCovariance function estimation Sparse functional PCA Sparse functional regression Problems Functional time seriesFundamental concepts of time series analysis Functional autoregressive process Forecasting with the Hyndman-Ullah methodForecasting with multivariate predictors Long-run covariance function Testing stationarity of functional time series Generation and estimation of the FAR(1) model using package fda Conditions for the existence of the FAR(1) process Further reading and other topics Problems Spatial functional data and modelsFundamental concepts of spatial statistics Functional spatial fields Functional kriging Mean function estimation Implementation in the R package geofd Other topics and further reading Problems Elements of Hilbert space theoryHilbert space Projections and orthonormal sets Linear operators Basics of spectral theory Tensors ProblemsRandom functions Random elements in metric spaces Expectation and covariance in a Hilbert space Gaussian functions and limit theorems Functional principal components ProblemsInference from a random sampleConsistency of sample mean and covariance functions Estimated functional principal components Asymptotic normality Hypothesis testing about the mean Confidence bands for the mean Application to BOA cumulative returns Proof of Theorem Problems

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Book SynopsisGeneralized Linear Mixed Models: Modern Concepts, Methods, and Applications (2nd edition) presents an updated introduction to linear modeling using the generalized linear mixed model (GLMM) as the overarching conceptual framework. For students new to statistical modeling, this book helps them see the big picture linear modeling as broadly understood and its intimate connection with statistical design and mathematical statistics. For readers experienced in statistical practice, but new to GLMMs, the book provides a comprehensive introduction to GLMM methodology and its underlying theory.Unlike textbooks that focus on classical linear models or generalized linear models or mixed models, this book covers all of the above as members of a unified GLMM family of linear models. In addition to essential theory and methodology, this book features a rich collection of examples using SAS software to illustrate GLMM practice. This second edition is updated to reflect les

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Book SynopsisElementary Probability with Applications, Second Edition shows students how probability has practical uses in many different fields, such as business, politics, and sports. In the book, students learn about probability concepts from real-world examples rather than theory.The text explains how probability models with underlying assumptions are used to model actual situations. It contains examples of probability models as they relate to: Bloc voting Population genetics Doubling strategies in casinos Machine reliability Airline management Cryptology Blood testing Dogs resembling owners Drug detection Jury verdicts Coincidences Number of concert hall aisles 2000 U.S. presidential election Points after deuce in tennis Tests regarding intelligent dogs Music composition Based on the author's course at TheTrade ReviewPraise for the First Edition:"It is desirable that all citizens have an elementary understanding of probability [so] that they better appreciate the uncertainty that surrounds them. Anyone teaching such citizens should consider using this book because, through its applications, it conveys something of the power of probability."—D.V. Lindley, Mathematical Gazette, November 2006"This book was surprisingly refreshing and did a great job using real situations to motivate techniques for calculating discrete probabilities."—Technometrics, August 2006"The problems are graded from fairly straightforward to very challenging and the book is, for this reason at least, a welcome addition to the literature."—MAA Reviews, October 2005"This book would be excellent for a minicourse at a higher level of high school as well as a semester course at the college level."—Larry White, National Council of Teachers of Mathematics (NCTM), October 2005"The chosen approach is practical and entertaining. The book is a useful tool for teachers and anybody interested in basic ideas and applications of classical probability theory."—EMS Newsletter, June 2005Table of ContentsBasic Concepts in Probability. Conditional Probability and the Multiplication Rule. Independence. Combining the Addition and Multiplication Rules. Combining the Addition and Multiplication Rules—Applications. Random Variables, Distributions, and Expected Values. Joint Distributions and Conditional Expectations. Sampling without Replacement. Sampling with Replacement. Sampling with Replacement (Continued). Binomial Tests. Appendix. Short Answers to Selected Exercises. Bibliography. Index.

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