Probability and statistics Books
Stata Press Flexible Parametric Survival Analysis Using
Book SynopsisThrough real-world case studies, this book shows how to use Stata to estimate a class of flexible parametric survival models. It discusses the modeling of time-dependent and continuous covariates and looks at how relative survival can be used to measure mortality associated with a particular disease when the cause of death has not been recorded. The book describes simple quantification of differences between any two covariate patterns through calculation of time-dependent hazard ratios, hazard differences, and survival differences.Table of ContentsIntroduction Goals A brief review of the Cox proportional hazards model Beyond the Cox model Why parametric models? Why not standard parametric models? A brief introduction to stpm Basic relationships in survival analysis Comparing models The delta method Ado-file resources How our book is organized Using stset and stsplit What is the stset command? Some key concepts Syntax of the stset command Variables created by the stset command Examples of using stset The stsplit command Conclusion Graphical introduction to the principal datasets Introduction Rotterdam breast cancer data England and Wales breast cancer data Orchiectomy data Conclusion Poisson models Introduction Modeling rates with the Poisson distribution Splitting the time scale Collapsing the data to speed up computation Splitting at unique failure times Comparing a different number of intervals Fine splitting of the time scale Splines: Motivation and definition FPs: Motivation and definition Discussion Royston–Parmar models Motivation and introduction Proportional hazards models Selecting a spline function PO models Probit models Royston–Parmar (RP) models Concluding remarks Prognostic models Introduction Developing and reporting a prognostic model What does the baseline hazard function mean? Model selection Quantitative outputs from the model Goodness of fit Out-of-sample prediction: Concept and applications Visualization of survival times Discussion Time-dependent effects Introduction Definitions What do we mean by a TD effect? Proportional on which scale? Poisson models with TD effects RP models with TD effects TD effects for continuous variables Attained age as the time scale Multiple time scales Prognostic models with TD effects Discussion Relative survival Introduction What is relative survival? Excess mortality and relative survival Motivating example Life-table estimation of relative survival Poisson models for relative survival RP models for relative survival Some comments on model selection Age as a continuous variable Concluding remarks Further topics Introduction Number needed to treat Average and adjusted survival curves Modeling distributions with RP models Multiple events Bayesian RP models Competing risks Period analysis Crude probability of death from relative survival models Final remarks References Author index Subject index
£72.19
Stata Press Data Analysis Using Stata, Third Edition
Book SynopsisData Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks.The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand.Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills. Table of ContentsThe First Time. Working with Do-Files. The Grammar of Stata. General Comments on the Statistical Commands. Creating and Changing Variables. Creating and Changing Graphs. Describing and Comparing Distributions. Statistical Inference. Introduction to Linear Regression. Regression Models for Categorical Dependent Variables. Reading and Writing Data. Do-Files for Advanced Users and User-Written Programs. Around Stata. References. Indices.
£69.34
Stata Press Multilevel and Longitudinal Modeling Using Stata,
Book SynopsisMultilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped. With comprehensive coverage, researchers who need to apply multilevel models will find this book to be the perfect companion. It is also the ideal text for courses in multilevel modeling because it provides examples from a variety of disciplines as well as end-of-chapter exercises that allow students to practice newly learned material. The book comprises two volumes. Volume I focuses on linear models for continuous outcomes.Table of ContentsI. Preliminaries 1. Review of linear regression II. Two-level models 3. Random-intercept models with covariates 4. Random-coefficient models III. Models for longitudinal and panel data; Introduction to models for longitudinal and panel data (part III) 5. Subject-specific effects and dynamic models 6. Marginal models 7. Growth-curve models IV. Models with nested and crossed random effects 8. Higher-level models with nested random effects 9. Crossed random effects
£66.49
Stata Press Multilevel and Longitudinal Modeling Using Stata,
Book SynopsisMultilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped. With comprehensive coverage, researchers who need to apply multilevel models will find this book to be the perfect companion. It is also the ideal text for courses in multilevel modeling because it provides examples from a variety of disciplines as well as end-of-chapter exercises that allow students to practice newly learned material. The book comprises two volumes. Volume II focuses on generalized linear models for binary, ordinal, count, and other types of outcomes.Table of ContentsVolume II: V. Models for categorical responses 10. Dichotomous or binary responses 11. Ordinal responses 12. Nominal responses and discrete choice VI. Models for counts 13. Counts VII. Models for survival or duration data; Introduction to models for survival or duration data (part VII) 14. Discrete-time survival 15. Continuous-time survival VIII. Models with nested and crossed random effects 16. Models with nested and crossed random effects
£66.49
Stata Press A Course in Item Response Theory and Modeling
Book SynopsisOver the past several decades, item response theory (IRT) and item response modeling (IRM) have become increasingly popular in the behavioral, educational, social, business, marketing, clinical, and health sciences. In this book, Raykov and Marcoulides begin with a nontraditional approach to IRT and IRM that is based on their connections to classical test theory, (nonlinear) factor analysis, generalized linear modeling, and logistic regression. Application-oriented discussions follow next. These cover the one-, two-, and three-parameter logistic models, polytomous item response models (with nominal or ordinal items), item and test information functions, instrument construction and development, hybrid models, differential item functioning, and an introduction to multidimensionalIRT and IRM. The pertinent analytic and modeling capabilities of Stata are thoroughly discussed, highlighted, and illustrated on empirical examples from behavioral and social research.Table of ContentsNotation and typography. What is item response theory and item response modeling? Two basic functions for item response theory and item response. Classical test theory, factor analysis, and their connections to item response theory Generalized linear modeling, logistic regression, nonlinear factor analysis, and their links to item response theory and item response modeling. Fundamentals of item response theory and item response modeling. First applications of Stata for item response modeling. Item response theory model fitting and estimation. Information functions and test characteristic curves Instrument construction and development using information functions. Differential item functioning. Polytomous item response models and hybrid models. Introduction to multidimensional item response theory and modeling
£53.19
Stata Press Stata Tips Fourth Edition Volumes I and II
Book SynopsisStata Tips provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata.The book comprises the contributions of the Stata community that have appeared in the Stata Journal since 2003. Each tip is a brief article that provides practical advice on using Stata. With tips covering a breadth of topics in statistics, graphics, data management, and programming, both new and experienced Stata users are sure to find tips that will be useful in their research.
£63.64
Stata Press Maximum Likelihood Estimation with Stata, Fifth
Book SynopsisMaximum Likelihood Estimation with Stata, Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Stata’s commands for writing ML estimators, the book presents an overview of the underpinnings of maximum likelihood and how to think about ML estimation.The fifth edition includes a new second chapter that demonstrates the easy-to-use mlexp command. This command allows you to directly specify a likelihood function and perform estimation without any programming.The core of the book focuses on Stata's ml command. It shows you how to take full advantage of ml’s noteworthy features: Linear constraints Four optimization algorithms (Newton–Raphson, DFP, BFGS, and BHHH) Observed information matrix (OIM) variance estimator Outer product of gradients (OPG) variance estimator Huber/White/sandwich robust variance estimator Cluster–robust variance estimator Complete and automatic support for survey data analysis Direct support of evaluator functions written in Mata When appropriate options are used, many of these features are provided automatically by ml and require no special programming or intervention by the researcher writing the estimator.In later chapters, you will learn how to take advantage of Mata, Stata's matrix programming language. For ease of programming and potential speed improvements, you can write your likelihood-evaluator program in Mata and continue to use ml to control the maximization process. A new chapter in the fifth edition shows how you can use the moptimize() suite of Mata functions if you want to implement your maximum likelihood estimator entirely within Mata.In the final chapter, the authors illustrate the major steps required to get from log-likelihood function to fully operational estimation command. This is done using several different models: logit and probit, linear regression, Weibull regression, the Cox proportional hazards model, random-effects regression, and seemingly unrelated regression. This edition adds a new example of a bivariate Poisson model, a model that is not available otherwise in Stata.The authors provide extensive advice for developing your own estimation commands. With a little care and the help of this book, users will be able to write their own estimation commands---commands that look and behave just like the official estimation commands in Stata.Whether you want to fit a special ML estimator for your own research or wish to write a general-purpose ML estimator for others to use, you need this book.Table of ContentsTheory and practice The likelihood-maximization problem Likelihood theory The maximization problem Estimation with mlexp Syntax Normal linear regression Initial values Restricted parameters Robust standard errors The probit model Specifying derivatives Additional estimation features Wrapping up Introduction to ml The probit mode Normal linear regression Robust standard errors Weighted estimation Other features of method-gf0 evaluators Limitations Overview of ml The terminology of ml Equations in ml Likelihood-evaluator methods Tools for the ml programmer Common ml options Maximizing your own likelihood functions Appendix: More about scalar parameters Method lf The linear-form restrictions Examples The importance of generating temporary variables as doubles Problems you can safely ignore Nonlinear specifications The advantages of lf in terms of execution speed Methods lf0, lf1, and lf2 Comparing these methods Outline of evaluators of methods lf0, lf1, and lf2 Summary of methods lf0, lf1, and lf2 Examples Methods d0, d1, and d2 Comparing these methods Outline of method d0, d1, and d2 evaluators Summary of methods d0, d1, and d2 Panel-data likelihoods Other models that do not meet the linear-form restrictions Debugging likelihood evaluators ml check Using the debug methods ml trace Setting initial values ml search ml plot ml init Interactive maximization The iteration log Pressing the Break key Maximizing difficult likelihood functions Final results Graphing convergence Redisplaying output Writing do-files to maximize likelihoods The structure of a do-file Putting the do-file into production Writing ado-files to maximize likelihoods Writing estimation commands The standard estimation-command outline Outline for estimation commands using ml Using ml in noninteractive mode Advice Writing ado-files for survey data analysis Program properties Writing your own predict command Mata-based likelihood evaluators Introductory examples Evaluator function prototypes Utilities Random-effects linear regression Ado-file considerations Mata’s moptimize() function Introductory examples Restricting the estimation sample Estimation preliminaries Estimation Results Estimation commands Regression redux Other examples The logit model The probit model Normal linear regression The Weibull model The Cox proportional hazards model The random-effects regression model The seemingly unrelated regression model A bivariate Poisson regression model Epilogue Syntax of mlexp Syntax of ml Syntax of moptimize() Likelihood-evaluator checklists Method lf Method d0 Method d1 Method d2 Method lf0 Method lf1 Method lf2 Listing of estimation commands The logit model The probit model The normal model The Weibull model The Cox proportional hazards model The random-effects regression model The seemingly unrelated regression model A bivariate Poisson regression model References
£56.99
Information Age Publishing Advances in Latent Class Analysis: A Festschrift
Book SynopsisWhat is latent class analysis? If you asked that question thirty or forty years ago you would have gotten a different answer than you would today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests within much broader mixture and diagnostic modeling framework, integrating measured and latent variables that may be categorical and/or continuous, and where latent classes serve to define the subpopulations for whom many aspects of the focal measured and latent variable model may differ.For latent class analysis to take these developmental leaps required contributions that were methodological, certainly, as well as didactic. Among the leaders on both fronts was C. Mitchell “Chan” Dayton, at the University of Maryland, whose work in latent class analysis spanning several decades helped the method to expand and reach its current potential. The current volume in the Center for Integrated Latent Variable Research (CILVR) series reflects the diversity that is latent class analysis today, celebrating work related to, made possible by, and inspired by Chan’s noted contributions, and signaling the even more exciting future yet to come.
£44.96
Tutorial Introductions Information Theory: A Tutorial Introduction
Book Synopsis
£62.96
CABI Publishing Practical R for Biologists: An Introduction
Book SynopsisR is a freely available, open-source statistical programming environment which provides powerful statistical analysis tools and graphics outputs. R is now used by a very wide range of people; biologists (the primary audience of this book), but also all other scientists and engineers, economists, market researchers and medical professionals. R users with expertise are constantly adding new associated packages, and the range already available is immense. This text works through a set of studies that collectively represent almost all the R operations that biology students need in order to analyse their own data. The material is designed to serve students from first year undergraduates through to those beginning post graduate levels. Chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping, and text parsing. Examples are based on real scientific studies, and each one covers the use of more R functions than those simply necessary to get a p-value or plot. The book walks the reader through the data analysis process, starting with very simple plots, and continuing through more complex analyses and programming. It shows how to deal with issues such as error messages that can be confronting for beginners, in order to set students up for a successful scientific career using R. Collectively the authors have a vast amount of teaching experience which they apply here to make the passage into R programming as gentle and easy as possible, whilst guiding the reader to tackle quite complicated programming.Table of Contents1: How to Use this Book 2: Installing and Running R 3: Very Basic R Syntax 4: First Simple Programs and Graphics 5: The Dataframe Concept 6: Plotting Biological Data in Various Ways 7: The Grammar of Graphics Family of Packages 8: Sets and Venn diagrams 9: Statistics: Choosing the Right Test 10: Commonly Used Measures and Statistical Tests 11: Regression and Correlation Analyses 12: Count Data as Response Variable 13: Analysis of Variance (ANOVA) 14: Analysis of Covariance (ANCOVA) 15: More Generalised Linear Modelling 16: Monte Carlo Tests and Randomisation 17: Principal Components Analysis 18: Species Abundance, Accumulation and Diversity Data 19: Survivorship 20: Dates and Julian Dates 21: Mapping and Parsing Text Input for Data 22: More on Manipulating Text 23: Phylogenies and Trees 24: Working with DNA Sequences and other character data 25: Spacing in Two Dimensions 26: Population Modelling Including Spatially Explicit Models 27: More on “apply” Family of Functions – Avoid Loops to get More Speed 28: Food webs and simple graphics 29: Adding Photographs 30: Standard Distributions in R 31: Reading and Writing Data to and from Files
£40.52
Pearson Education Limited Edexcel GCSE Statistics Student Book
Book SynopsisEverything a student needs to ensure exam success Written by Chief examiners and experienced teachers. Revised and enhanced following user feedback on the 2001 Heinemann edition. Practice exam papers for foundation and higher, exactly matched to the new specification. Three revision exercises, featuring past exam questions, consolidate learning on groups of topics. examzone section gives tips, tests and techniques for exam preparation and the new controlled assessment.
£42.48
Springer London Ltd An Introduction to Statistical Modeling of
Book SynopsisDirectly oriented towards real practical application, this book develops both the basic theoretical framework of extreme value models and the statistical inferential techniques for using these models in practice. Intended for statisticians and non-statisticians alike, the theoretical treatment is elementary, with heuristics often replacing detailed mathematical proof. Most aspects of extreme modeling techniques are covered, including historical techniques (still widely used) and contemporary techniques based on point process models. A wide range of worked examples, using genuine datasets, illustrate the various modeling procedures and a concluding chapter provides a brief introduction to a number of more advanced topics, including Bayesian inference and spatial extremes. All the computations are carried out using S-PLUS, and the corresponding datasets and functions are available via the Internet for readers to recreate examples for themselves. An essential reference for students and researchers in statistics and disciplines such as engineering, finance and environmental science, this book will also appeal to practitioners looking for practical help in solving real problems. Stuart Coles is Reader in Statistics at the University of Bristol, UK, having previously lectured at the universities of Nottingham and Lancaster. In 1992 he was the first recipient of the Royal Statistical Society's research prize. He has published widely in the statistical literature, principally in the area of extreme value modeling.Trade ReviewFrom the reviews of the first edition:JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION"Coles is to be congratulated on having brought the whole breadth of statistical modeling extremes within one volume of about 200 pages. This is indeed a nontrivial feat…I am convinced that this book will find its rightful place on the extremal-event modeler’s bookshelf. The very readable style, the many examples, and the avoidance of too many technicalities will no doubt please numerous researchers and students who want to apply the theory in their own research environment.""This book is all about the theory and applications of extreme value models. … Both statisticians and applied scientists in engineering, finance, traffic analysts, food scientists, earthquake engineers, and environmental scientists will like this book. I enjoyed reading it and recommend it highly." (Ramalingam Shanmugam, Journal of Statistical Computation and Stimulation, Vol. 74 (11), 2004) "In the given book, Stuart Coles presents his viewpoint of the methodology which is necessary for applying extreme value theory in the univariate and multivariate case. … The author covers quite a lot of material on just 208 pages. The main ideas of extreme value theory are clearly elaborated. … For the reviewer it was enjoyable to read this book." (Rolf-Dieter Reiss, Metrika, February, 2003)"Coles is to be congratulated on having brought the whole breadth of statistical modeling of extremes within one volume of about 200 pages. … I am convinced that this book will find its rightful place on the extremal-event modeler’s bookshelf. The very readable style, the many examples, and the avoidance of too many technicalities will no doubt please numerous researchers and students who want to apply the theory in their own research environment." (Paul Embrechts, JASA, December, 2002)"The modeling of extreme values is important to scientists in such fields as hydrology, civil engineering, environmental science, oceanography and finance. Stuart Coles’s book on the modeling of extreme values provides an introductory text on the topic. … The book is meant for individuals with moderate statistical background. … Overall, this is a good text for someone getting started in extreme value methods." (Eric P. Smith, Technometrics, Vol. 44 (4), 2002)"This is a truly enjoyable introduction with a collection of 11 highly motivating data sets and an excellent, clear, discussion of the probabilistic framework and associated inferential techniques with minimal use of notations. … In summary, this is a highly welcome monograph recommended for the personal collection of anyone who plans to interact with extreme value data." (H. N. Nagaraja, Zentralblatt MATH, Vol. 980, 2002)Table of Contents1. Introduction.- 2. Basics of Statistical Modeling.- 3. Classical Extreme Value Theory and Models.- 4. Threshold Models.- 5. Extremes of Dependent Sequences.- 6. Extremes of Non-Stationary Sequences.- 7. A Point Process Characterization of Extremes.- 8. Multivariate Extremes.- 9. Further Topics.- Appendix A: Computational Aspects.- Index.
£999.99
Springer Nature Switzerland AG Stochastic Programming: Modeling Decision Problems Under Uncertainty
Book SynopsisThis book provides an essential introduction to Stochastic Programming, especially intended for graduate students. The book begins by exploring a linear programming problem with random parameters, representing a decision problem under uncertainty. Several models for this problem are presented, including the main ones used in Stochastic Programming: recourse models and chance constraint models. The book not only discusses the theoretical properties of these models and algorithms for solving them, but also explains the intrinsic differences between the models. In the book’s closing section, several case studies are presented, helping students apply the theory covered to practical problems. The book is based on lecture notes developed for an Econometrics and Operations Research course for master students at the University of Groningen, the Netherlands - the longest-standing Stochastic Programming course worldwide. Trade Review“The book is well written. The book will be of interest to mathematicians, engineers, economics and especially graduate students.” (I. M. Stancu-Minasian, zbMATH 1446.90118, 2020)Table of ContentsIntroduction.- Random Objective Functions.- Recourse Models.- Stochastic Mixed-integer Programming.- Chance Constraints.- Integrated Chance Constraints.- Assignments.- Case Studies.
£54.99
Springer Nature Switzerland AG Statistical Analysis of Network Data with R
Book SynopsisThe new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks. The book begins by covering tools for the manipulation of network data. Next, it addresses visualization and characterization of networks. The book then examines mathematical and statistical network modeling. This is followed by a special case of network modeling wherein the network topology must be inferred. Network processes, both static and dynamic are addressed in the subsequent chapters. The book concludes by featuring chapters on network flows, dynamic networks, and networked experiments. Statistical Analysis of Network Data with R, 2nd Ed. has been written at a level aimed at graduate students and researchers in quantitative disciplines engaged in the statistical analysis of network data, although advanced undergraduates already comfortable with R should find the book fairly accessible as well.Table of Contents1 Introduction.- 2 Manipulating Network Data.- 3 Visualizing Network Data.- 4 Descriptive Analysis of Network Graph Characteristics.- 5 Mathematical Models for Network Graphs.- 6 Statistical Models for Network Graphs.- 7 Network Topology Inference.- 8 Modeling and Prediction for Processes on Network Graphs.- 9 Analysis of Network Flow Data.- 10 Networked Experiments.- 11 Dynamic Networks.- Index.
£56.99
Springer Nature Switzerland AG Methods and Applications of Sample Size
Book SynopsisThis book provides an extensive overview of the principles and methods of sample size calculation and recalculation in clinical trials. Appropriate calculation of the required sample size is crucial for the success of clinical trials. At the same time, a sample size that is too small or too large is problematic due to ethical, scientific, and economic reasons. Therefore, state-of-the art methods are required when planning clinical trials. Part I describes a general framework for deriving sample size calculation procedures. This enables an understanding of the common principles underlying the numerous methods presented in the following chapters. Part II addresses the fixed sample size design, where the required sample size is determined in the planning stage and is not changed afterwards. It covers sample size calculation methods for superiority, non-inferiority, and equivalence trials, as well as comparisons between two and more than two groups. A wide range of further topics is discussed, including sample size calculation for multiple comparisons, safety assessment, and multi-regional trials. There is often some uncertainty about the assumptions to be made when calculating the sample size upfront. Part III presents methods that allow to modify the initially specified sample size based on new information that becomes available during the ongoing trial. Blinded sample size recalculation procedures for internal pilot study designs are considered, as well as methods for sample size reassessment in adaptive designs that use unblinded data from interim analyses. The application is illustrated using numerous clinical trial examples, and software code implementing the methods is provided. The book offers theoretical background and practical advice for biostatisticians and clinicians from the pharmaceutical industry and academia who are involved in clinical trials. Covering basic as well as more advanced and recently developed methods, it is suitable for beginners, experienced applied statisticians, and practitioners. To gain maximum benefit, readers should be familiar with introductory statistics. The content of this book has been successfully used for courses on the topic.Trade Review“The R source code is shown by chapter, well-documented, and easy to find and follow as brief descriptions and necessary specifications for the function calls are given by means of comments. … a wide area of application fields is covered and exhaustive literature references for further reading are given. … The presentation of the material is very reader-friendly, easily accessible and pedagogical … . It is likewise highly recommended … . This is an effective and nicely written reference textbook.” (Oke Gerke, ISCB News, iscb.info, Vol. 72, December, 2021)Table of ContentsPart I Basics 1 Introduction 1.1 Background and outline 1.2 Examples 1.2.1 The ChroPac trial 1.2.2 The Parkinson trial 1.3 General considerations when calculating sample sizes 2 Statistical test and sample size calculation 2.1 The main principle of statistical testing 2.2 The main principle of sample size calculation Part II Sample size calculation 3 Comparison of two groups for normally distributed outcomes and test for difference or superiority 3.1 Background and notation 3.2 z-test 3.3 t-test 3.4 Analysis of covariance 3.5 Bayesian approach 3.5.1 Background 3.5.2 Methods 4 Comparison of two groups for continuous and ordered categorical outcomes and test for difference or superiority 4.1 Background and notation 4.2 Continuous outcomes 4.3 Ordered categorical outcomes 4.3.1 Assumption-free approach 4.3.2 Assuming proportional odds 5 Comparison of two groups for binary outcomes and test for difference and superiority 5.1 Background and notation 5.2 Asymptotic tests 5.2.1 Difference of rates as effect measure 5.2.2 Risk ratio as effect measure 5.2.3 Odds ratio as effect measure 5.2.4 Logistic regression 5.3 Exact unconditional tests 5.3.1 Background 5.3.2 Fisher-Boschloo test 6 Comparison of two groups for time-to-event outcomes and test for differences or superiority 6.1 Background and notation 6.1.1 Time-to-event data 6.1.2 Sample size calculation for time-to-event data 6.2 Exponentially distributed time-to-event data 6.3 Time-to-event data with proportional hazards 6.3.1 Approach of Schoenfeld 6.3.2 Approach of Freedman 7 Comparison of more than two groups and test for difference 7.1 Background and notation 7.2 Normally distributed outcomes 7.3 Continuous outcomes 7.4 Binary outcomes 7.4.1 Analysis with chi-square test 7.4.2 Analysis with Cochran-Armitage test 7.5 Time-to-event outcomes 8 Comparison of two groups and test for non-inferiority 8.1 Background and notation 8.2 Normally distributed outcomes 8.2.1 Difference of means 8.2.2 Ratio of means 8.3 Continuous and ordered categorical outcomes 8.4 Binary outcomes 8.4.1 Analysis with asymptotic tests 8.4.1.1 Difference of rates as effect measure 8.4.1.2 Risk ratio as effect measure 8.4.1.3 Odds ratio as effect measure 8.4.2 Exact unconditional tests 8.4.2.1 Background 8.4.2.2 Difference of rates as effect measure 8.4.2.3 Risk ratio as effect measure 8.4.2.4 Odds ratio as effect measure 8.5 Time-to-event outcomes 9 Comparison of three groups in the gold standard non-inferiority design 9.1 Background and notation 9.2 Net effect approach 9.3 Fraction effect approach 10 Comparison of two groups for normally distributed outcomes and test for equivalence 10.1 Background and notation 10.2 Difference of means 10.3 Ratio of means 11 Multiple comparisons 11.1 Background and notation 11.2 Generally applicable sample size calculation methods and applications 11.2.1 Methods 11.2.2 Applications 11.3 Multiple endpoints 11.3.1 Background and notation 11.3.2 Methods 11.4 More than two groups 11.4.1 Background and notation 11.4.2 Dunnett test 12 Assessment of safety 12.1 Background and notation 12.2 Testing hypotheses on the event probability 12.3 Estimating the occurrence probability of an event with specified precision 12.4 Observing at least one event 13 Cluster-randomized trials 13.1 Background and notation 13.2 Normally distributed outcomes 13.2.1 Cluster-level analysis 13.2.2 Individual-level analysis 13.2.3 Dealing with unequal cluster size 13.3 Other scale levels of the outcome 14 Multi-regional trials 14.1 Background and notation 14.2 Sample size calculation for demonstrating consistency of global results and results for a specified region 14.3 Sample size calculation for demonstrating a consistent trend across all regions 15 Integrated planning of phase II/III drug development programs 15.1 Background and notation 15.2 Optimizing phase II/III programs 16 Simulation-based sample size calculation Part III Sample size recalculation 17 Background Part IIIA Blinded sample size recalculation in internal pilot study designs 18 Background and notation 19 A general approach for controlling the type I error rate for blinded sample size recalculation 20 Comparison of two groups for normally distributed outcomes and test for difference or superiority 20.1 t-Test 20.1.1 Background and notation 20.1.2 Blinded variance estimation 20.1.3 Type I error rate 20.1.4 Power and sample size 20.2 Analysis of covariance 20.2.1 Background and notation 20.2.2 Blinded variance estimation 20.2.3 Type I error rate 20.2.4 Power and sample size 21 Comparison of two groups for binary outcomes and test for difference or superiority 21.1 Background and notation 21.2 Asymptotic tests 21.2.1 Difference of rates as effect measure 21.2.2 Risk ratio and odds ratio as effect measure 21.3 Fisher-Boschloo test 22 Comparison of two groups for normally distributed outcomes and test for non-inferiority 22.1 t-Test 22.1.1 Background and notation 22.1.2 Blinded variance estimation 22.1.3 Type I error rate 22.1.4 Power and sample size 22.2 Analysis of covariance 23 Comparison of two groups for binary outcomes and test for non-inferiority 23.1 Background and notation 23.2 Difference of rates as effect measure 23.3 Risk ratio and odds ratio as effect measure 24 Comparison of two groups for normally distributed outcomes and test for equivalence 25 Regulatory and operational aspects 26 Concluding remarks Part IIIB Unblinded sample size recalculation in adaptive designs 27 Background and notation 27.1 Group-sequential designs 27.2 Adaptive designs 27.2.1 Combination function approach 27.2.2 Conditional error function approach 28 Sample size recalculation based on conditional power 28.1 Background and notation 28.2 Using the interim estimate of the effect 28.3 Using the initially specified effect 28.4 Using prior information as well as the interim effect estimate 29 Sample size recalculation by optimization 30 Regulatory and operational aspects 31 Concluding remarks Appendix: Selected R software code References
£49.49
Springer Nature Switzerland AG Linear Model Theory: Exercises and Solutions
Book SynopsisThis book contains 296 exercises and solutions covering a wide variety of topics in linear model theory, including generalized inverses, estimability, best linear unbiased estimation and prediction, ANOVA, confidence intervals, simultaneous confidence intervals, hypothesis testing, and variance component estimation. The models covered include the Gauss-Markov and Aitken models, mixed and random effects models, and the general mixed linear model. Given its content, the book will be useful for students and instructors alike. Readers can also consult the companion textbook Linear Model Theory - With Examples and Exercises by the same author for the theory behind the exercises.Trade Review“This volume contains solutions to the book's exercises … Many of those exercises stand as useful applications of results stated in the theory volume. Some of them go one step beyond and extend the theoretical results. I found this to be a very interesting and unique feature of the book on linear models, making the whole set particularly useful for both graduate students and instructors.” (Vassilis G. S. Vasdekis, Mathematical Reviews, August 2022)Table of Contents1 A Brief Introduction.- 2 Selected Matrix Algebra Topics and Results.- 3 Generalized Inverses and Solutions to Sytems of Linear Equations.- 4 Moments of a Random Vector and of Linear and Quadratic Forms in a Random Vector.- 5 Types of Linear Models.- 6 Estimability.- 7 Least Squares Estimation for the Gauss-Markov Model.- 8 Least Squares Geometry and the Overall ANOVA.- 9 Least Squares Estimation and ANOVA for Partitioned Models.- 10 Constrained Least Squares Estimation and ANOVA.- 11 Best Linear Unbiased Estimation for the Aitken Model.- 12 Model Misspecification.- 13 Best Linear Unbiased Prediction.- 14 Distribution Theory.- 15 Inference for Estimable and Predictable Functions.- 16 Inference for Variance-Covariance Parameters.- 17 Empirical BLUE and BLUP.
£104.49
Springer Nature Switzerland AG Probability Theory: A Comprehensive Course
Book SynopsisThis popular textbook, now in a revised and expanded third edition, presents a comprehensive course in modern probability theory.Probability plays an increasingly important role not only in mathematics, but also in physics, biology, finance and computer science, helping to understand phenomena such as magnetism, genetic diversity and market volatility, and also to construct efficient algorithms. Starting with the very basics, this textbook covers a wide variety of topics in probability, including many not usually found in introductory books, such as: limit theorems for sums of random variables martingales percolation Markov chains and electrical networks construction of stochastic processes Poisson point process and infinite divisibility large deviation principles and statistical physics Brownian motion stochastic integrals and stochastic differential equations. The presentation is self-contained and mathematically rigorous, with the material on probability theory interspersed with chapters on measure theory to better illustrate the power of abstract concepts.This third edition has been carefully extended and includes new features, such as concise summaries at the end of each section and additional questions to encourage self-reflection, as well as updates to the figures and computer simulations. With a wealth of examples and more than 290 exercises, as well as biographical details of key mathematicians, it will be of use to students and researchers in mathematics, statistics, physics, computer science, economics and biology.Table of Contents1 Basic Measure Theory.- 2 Independence.- 3 Generating Functions.- 4 The Integral.- 5 Moments and Laws of Large Numbers.- 6 Convergence Theorems.- 7 Lp-Spaces and the Radon–Nikodym Theorem.- 8 Conditional Expectations.- 9 Martingales.- 10 Optional Sampling Theorems.- 11 Martingale Convergence Theorems and Their Applications.- 12 Backwards Martingales and Exchangeability.- 13 Convergence of Measures.- 14 Probability Measures on Product Spaces.- 15 Characteristic Functions and the Central Limit Theorem.- 16 Infinitely Divisible Distributions.- 17 Markov Chains.- 18 Convergence of Markov Chains.- 19 Markov Chains and Electrical Networks.- 20 Ergodic Theory.- 21 Brownian Motion.- 22 Law of the Iterated Logarithm.- 23 Large Deviations.- 24 The Poisson Point Process.- 25 The Itô Integral.- 26 Stochastic Differential Equations.- References.- Notation Index.- Name Index.- Subject Index.
£52.24
Springer Nature Switzerland AG Excel 2019 in Applied Statistics for High School Students: A Guide to Solving Practical Problems
Book SynopsisThis textbook is a step-by-step guide for high school, community college, and undergraduate students who are taking a course in applied statistics and wish to learn how to use Excel to solve statistical problems. All of the statistics problems in this book come from the following fields of study: business, education, psychology, marketing, engineering and advertising. Students will learn how to perform key statistical tests in Excel without being overwhelmed by statistical theory. Each chapter briefly explains a topic and then demonstrates how to use Excel commands and formulas to solve specific statistics problems. The book offers guidance in using Excel in two different ways: (1) writing formulas (e.g., confidence interval about the mean, one-group t-test, two-group t-test, correlation) and (2) using Excel’s drop-down formula menus (e.g., simple linear regression, multiple correlations and multiple regression, and one-way ANOVA). Three practice problems are provided at the end of each chapter, along with their solutions in an appendix. An additional practice test allows readers to test their understanding of each chapter by attempting to solve a specific statistics problem using Excel; the solution to each of these problems is also given in an appendix. This book is a tool that can be used either by itself or along with any good statistics book.Table of ContentsPreface.- Acknowledgements.- 1 Sample Size, Mean, Standard Deviation, and Standard Error of the Mean.- 2 Random Number Generator.- 3 Confidence Interval About the Mean Using the TINV Function and Hypothesis Testing.- 4 One-Group t-Test for the Mean.- 5 Two-Group t-Test of the Difference of the Means for Independent Groups.- 6 Correlation and Simple Linear Regression.- 7 Multiple Correlation and Multiple Regression.- 8 One-Way Analysis of Variance (ANOVA).- Appendix A: Answers to End-of-Chapter Practice Problems.- Appendix B: Practice Test.- Appendix C: Answers to Practice Test.- Appendix D: Statistical Formulas.- Appendix E: t-table.- Index.
£54.99
Springer Nature Switzerland AG Extreme Value Theory with Applications to Natural
Book SynopsisThis richly illustrated book describes statistical extreme value theory for the quantification of natural hazards, such as strong winds, floods and rainfall, and discusses an interdisciplinary approach to allow the theoretical methods to be applied. The approach consists of a number of steps: data selection and correction, non-stationary theory (to account for trends due to climate change), and selecting appropriate estimation techniques based on both decision-theoretic features (e.g., Bayesian theory), empirical robustness and a valid treatment of uncertainties. It also examines and critically reviews alternative approaches based on stochastic and dynamic numerical models, as well as recently emerging data analysis issues and presents large-scale, multidisciplinary, state-of-the-art case studies. Intended for all those with a basic knowledge of statistical methods interested in the quantification of natural hazards, the book is also a valuable resource for engineers conducting risk analyses in collaboration with scientists from other fields (such as hydrologists, meteorologists, climatologists). Table of Contents1 E. Garnier: Extreme Events and History: for a better consideration of natural hazards.- 2 N. Bousquet and P. Bernardara: Introduction.- Part I Standard Extreme Value Theory.- 3 P. Bernardara and N. Bousquet: Probabilistic modeling and statistical quantification of natural hazards.- 4 N. Bousquet: Fundamental concepts of probability and statistics.- 5 M. Andreewsky and N. Bousquet: Collecting and analyzing data.- 6 A. Dutfoy: Univariate extreme value theory: practice and limitations.- Part II Elements of Extensive Statistical Analysis.- 7 J. Weiss and M. Andreewsky: Regional extreme value analysis.- 8 S. Parey, T. Hoang: Extreme values of non-stationary time series.- 9 A. Dutfoy: Multivariate extreme value theory: practice and limits.- 10 S., T. Hoang and N. Bousquet: Stochastic and physics-based simulation of extreme situations.- 11 N. Bousquet: Bayesian extreme value theory.- 12 M. Andreewsky, P. Bernardara, N. Bousquet, A. Dutfoy and S. Parey: Perspectives.- Part III Detailed Case Studies on Natural Hazards.- 13 P. Bernardara: Predicting extreme ocean swells.- 14 M. Andreewsky: Predicting storm surges.- 15 S. Parey: Forecasting extreme winds.- 16 N. Roche and A. Dutfoy: Conjunction of rainfall in neighboring watersheds.- 17 A. Sibler and A. Dutfoy: Conjunction of a flood and a storm.- 18 E. Paquet: SCHADEX: an alternative to extreme value statistics in hydrology.- Appendix A.- Appendix B.- References.- Index.
£142.49
Springer Nature Switzerland AG High-Dimensional Covariance Matrix Estimation: An Introduction to Random Matrix Theory
Book SynopsisThis book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.Table of ContentsForeword.- 1 Introduction.- 2 Traditional Estimators and Standard Asymptotics.- 3 Finite Sample Performance of Traditional Estimators.- 4 Traditional Estimators and High-Dimensional Asymptotics.- 5 Summary and Outlook.- Appendices.
£52.24
Springer Nature Switzerland AG Solutions Manual for Econometrics
Book SynopsisThis Fourth Edition updates the "Solutions Manual for Econometrics" to match the Sixth Edition of the Econometrics textbook. It adds problems and solutions using latest software versions of Stata and EViews. Special features include empirical examples replicated using EViews, Stata as well as SAS. The book offers rigorous proofs and treatment of difficult econometrics concepts in a simple and clear way, and provides the reader with both applied and theoretical econometrics problems along with their solutions. These should prove useful to students and instructors using this book.Table of ContentsWhat Is Econometrics?.- A Review of Some Basic Statistical Concepts.- Simple Linear Regression.- Multiple Regression Analysis.- Violations of the Classical Assumptions.- Distributed Lags and Dynamic Models.- The General Linear Model: The Basics.- Regression Diagnostics and Specification Tests.- Generalized Least Squares.- Seemingly Unrelated Regressions.- Simultaneous Equations Model.- Pooling Time-Series of Cross-Section Data.- Limited Dependent Variables.- Time-Series Analysis.
£39.59
Springer Nature Switzerland AG Epistemic Processes: A Basis for Statistics and
Book SynopsisThis book discusses a link between statistical theory and quantum theory based on the concept of epistemic processes. The latter are processes, such as statistical investigations or quantum mechanical measurements, that can be used to obtain knowledge about something. Various topics in quantum theory are addressed, including the construction of a Hilbert space from reasonable assumptions and an interpretation of quantum states. Separate derivations of the Born formula and the one-dimensional Schrödinger equation are given. In concrete terms, a Hilbert space can be constructed under some technical assumptions associated with situations where there are two conceptual variables that can be seen as maximally accessible. Then to every accessible conceptual variable there corresponds an operator on this Hilbert space, and if the variables take a finite number of values, the eigenspaces/eigenvectors of these operators correspond to specific questions in nature together with sharp answers to these questions. This paves a new way to the foundations of quantum theory. The resulting interpretation of quantum mechanics is related to Hervé Zwirn's recent Convivial Solipsism, but it also has some relations to Quantum Bayesianism and to Rovelli's relational quantum mechanics. Niels Bohr's concept of complementarity plays an important role. Philosophical implications of this approach to quantum theory are discussed, including consequences for macroscopic settings.The book will benefit a broad readership, including physicists and statisticians interested in the foundations of their disciplines, philosophers of science and graduate students, and anyone with a reasonably good background in mathematics and an open mind.Table of Contents1. The epistemic view upon science.- 2. Statistical inference.- 3. Inference in an epistemic process.- 4. Towards quantum theory.- 5. Aspects of quantum theory.- 6. Macroscopic consequences.
£71.24
Springer Nature Switzerland AG Data Warehousing and Analytics: Fueling the Data Engine
Book SynopsisThis textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge.The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics).This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.Table of Contents1. Introduction.- Part I: Star Schema.- 2. Simple Star Schemas.- 3. Creating Facts and Dimensions: More Complex Processes.- Part II: Snowflake and Bridge Tables.- 4. Hierarchies.- 5. Bridge Tables.- 6. Temporal Data Warehousing.- Part III: Advanced Dimension.- 7. Determinant Dimensions.- 8. Junk Dimensions.- 9. Dimension Keys.- 10. One-Attribute Dimensions.- Part IV: Multi-Fact and Multi-Input.- 11. Multi-Fact Star Schemas.- 12. Slicing a Fact.- 13. Multi-Input Operational Databases.- Part V: Data Warehousing Granularity and Evolution.- 14. Data Warehousing Granularity and Levels of Aggregation.- 15. Designing Lowest-Level Star Schemas.- 16. Levels of Aggregation: Adding and Removing Dimensions.- 17. Levels of Aggregation and Bridge Tables.- 18. Active Data Warehousing.- Part VI: OLAP, Business Intelligence, and Data Analytics.- 19. Online Analytical Processing (OLAP).- 20. Pre- and Post-Data Warehousing.- 21. Data Analytics for Data Warehousing.
£58.49
Springer Nature Switzerland AG Stochastic Benchmarking: Theory and Applications
Book SynopsisThis book introduces readers to benchmarking techniques in the stochastic environment, primarily stochastic data envelopment analysis (DEA), and provides stochastic models in DEA for the possibility of variations in inputs and outputs. It focuses on the application of theories and interpretations of the mathematical programs, which are combined with economic and organizational thinking. The book’s main purpose is to shed light on the advantages of the different methods in deterministic and stochastic environments and thoroughly prepare readers to properly use these methods in various cases. Simple examples, along with graphical illustrations and real-world applications in industry, are provided for a better understanding. The models introduced here can be easily used in both theoretical and real-world evaluations. This book is intended for graduate and PhD students, advanced consultants, and practitioners with an interest in quantitative performance evaluation.Table of Contents1. Benchmarking.- 2. An Introduction to Data Envelopment Analysis.- 3. Probability Theory.- 4. Stochastic Data Envelopment Analysis.- 5. Stochastic Network Data Envelopment Analysis.- 6. Stochastic Scale Elasticity.
£49.49
Springer International Publishing AG Introduction to Mathematics for Economics with R
Book SynopsisThis book provides a practical introduction to mathematics for economics using R software. Using R as a basis, this book guides the reader through foundational topics in linear algebra, calculus, and optimization. The book is organized in order of increasing difficulty, beginning with a rudimentary introduction to R and progressing through exercises that require the reader to code their own functions in R. All chapters include applications for topics in economics and econometrics. As fully reproducible book, this volume gives readers the opportunity to learn by doing and develop research skills as they go. As such, it is appropriate for students in economics and econometrics.Table of Contents1. Introduction to R.- 2. Linear Algebra.- 3. Functions of one variable.- 4. Dierential Calculus.- 5. Integral Calculus.- 6. Multivariable Calculus.- 7. Constrained Optimization.- 8. Trigonometry.- 9. Complex numbers.- 10. Difference equations.- 11. Differential equations.
£42.74
Springer International Publishing AG Mathematics and Statistics for Science
Book SynopsisMathematics and statistics are the bedrock of modern science. No matter which branch of science you plan to work in, you simply cannot avoid quantitative approaches. And while you won’t always need to know a great deal of theory, you will need to know how to apply mathematical and statistical methods in realistic scenarios. That is precisely what this book teaches. It covers the mathematical and statistical topics that are ubiquitous in early undergraduate courses, but does so in a way that is directly linked to science.Beginning with the use of units and functions, this book covers key topics such as complex numbers, vectors and matrices, differentiation (both single and multivariable), integration, elementary differential equations, probability, random variables, inference and linear regression. Each topic is illustrated with widely-used scientific equations (such as the ideal gas law or the Nernst equation) and real scientific data, often taken directly from recent scientific papers. The emphasis throughout is on practical solutions, including the use of computational tools (such as Wolfram Alpha or R), not theoretical development. There is a large number of exercises, divided into mathematical drills and scientific applications, and full solutions to all the exercises are available to instructors.Mathematics and Statistics for Science covers the core methods in mathematics and statistics necessary for a university degree in science, highlighting practical solutions and scientific applications. Its pragmatic approach is ideal for students who need to apply mathematics and statistics in a real scientific setting, whether in the physical sciences, life sciences or medicine.Table of ContentsPart I Units and Measurement.- 1 Units.- 2 Measurement, rounding and uncertainty.- Part II Functions and Complex Numbers.- 3 Functions.- 4 Exponential and log functions.- 5 Periodic functions.- 6 Linearising functions.- 7 Complex numbers.- Part III Vectors, Matrices and Linear Systems.- 8 Vectors.- 9 Matrices.- 10 Systems of linear equations.- 11 Solving systems of linear equations using matrices.- Part IV Differentiation: Functions of One Variable.- 12 Limits.- 13 Differentiation as a limit.- 14. Differentiation in practice.- 15 Numerical differentiation.- 16 Implicit differentiation.- 17 Maxima and minima.- Part V Differentiation: Functions of Multiple Variables.- 18 Functions of multiple variables.- 19 Partial derivatives.- 20 Extreme of functions of two (or more) variables.- Part VI Integration.- 21 The area under a curve.- 22 Calculating antiderivatives and areas.- 23 Integration techniques.- 24 Numerical integration.- Part VII Differential Equations.- 25 First-order ordinary differential equations.- 26 Numerical solutions of differential equations.- Part VIII Probability.- 27 Probability foundations.- 28 Random variables.- 29 Binomial distribution.- 30 Conditional probability.- 31 Total probability rule.- Part IX Statistical inference.- 32 Hypothesis test.- 33 Hypothesis testing in practice.- 34 Estimation and likelihood.- Part X Discrete Probability Distributions.- 35 Simulation and visualisation.- 36 Mean.- 37 Variance.- 38 Discrete probability models.- Part XI Continuous Probability Distributions.- 39 Continuous random variables.- 40 Common continuous probability models.- 41 Normal distribution and inference.- Part XII Linear Regression.- 42 Fitting linear functions: theory and practice.- 43 Quantifying relationships.- References.- Index.
£999.99
Springer International Publishing AG Continuous Time Processes for Finance: Switching, Self-exciting, Fractional and other Recent Dynamics
Book SynopsisThis book explores recent topics in quantitative finance with an emphasis on applications and calibration to time-series. This last aspect is often neglected in the existing mathematical finance literature while it is crucial for risk management. The first part of this book focuses on switching regime processes that allow to model economic cycles in financial markets. After a presentation of their mathematical features and applications to stocks and interest rates, the estimation with the Hamilton filter and Markov Chain Monte-Carlo algorithm (MCMC) is detailed. A second part focuses on self-excited processes for modeling the clustering of shocks in financial markets. These processes recently receive a lot of attention from researchers and we focus here on its econometric estimation and its simulation. A chapter is dedicated to estimation of stochastic volatility models. Two chapters are dedicated to the fractional Brownian motion and Gaussian fields. After a summary of their features, we present applications for stock and interest rate modeling. Two chapters focuses on sub-diffusions that allows to replicate illiquidity in financial markets. This book targets undergraduate students who have followed a first course of stochastic finance and practitioners as quantitative analyst or actuaries working in risk management.Trade Review“Hainaut has written a book which in such panorama has a position of its own and which should be considered with great interest. … the book should definitely be considered an excellent and warmly recommended read. It is likely that it will be soon become a reference for those interested in modern topics and for young researchers in particular.” (Gianluca Cassese, zbMATH 1512.91001, 2023)Table of ContentsPreface.- Acknowledgements.- Notations.- 1. Switching Models: Properties and Estimation.- 2. Estimation of Continuous Time Processes by Markov Chain Monte Carlo.- 3. Particle Filtering and Estimation.- 4. Modeling of Spillover Effects in Stock Markets.- 5. Non-Markov Models for Contagion and Spillover.- 6. Fractional Brownian Motion.- 7. Gaussian Fields for Asset Prices.- 8. Lévy Interest Rate Models With a Long Memory.- 9. Affine Volterra Processes and Rough Models.- 10. Sub-Diffusion for Illiquid Markets.- 11. A Fractional Dupire Equation for Jump-Diffusions.- References.
£104.49
Springer International Publishing AG Modern Biostatistical Methods for Evidence-Based
Book SynopsisThis book provides an overview of the emerging topics in biostatistical theories and methods through their applications to evidence-based global health research and decision-making. It brings together some of the top scholars engaged in biostatistical method development on global health to highlight and describe recent advances in evidence-based global health applications. The volume is composed of five main parts: data harmonization and analysis; systematic review and statistical meta-analysis; spatial-temporal modeling and disease mapping; Bayesian statistical modeling; and statistical methods for longitudinal data or survival data. It is designed to be illuminating and valuable to both expert biostatisticians and to health researchers engaged in methodological applications in evidence-based global health research. It is particularly relevant to countries where global health research is being rigorously conducted.Table of Contents1. Harmonization of Longitudinal Population Data: evidence from three rural health and demographic surveillance system nodes in South Africa.- 2. Adjusting for Selection Bias in Assessing the Efficacy of Health Inputs on Birth Outcome: Evidence from South-Saharan Africa.- 3. An Indirect Assessment of the Effect of Anthropogenic Activities on the Ecology of the Intermediate Snail Host for Schistosoma Haematobium.- 4. Diagonal Reference Modeling of Effects of Couples' Educational Differences on Women's Health Care Utilization in Sub-Saharan Africa - Gebrenegus Ghilagaber, Michael Carlson.- 5. Sequential Modeling of Parity Progression Ratios in Sub-Saharan Africa.- 6. Evidence-informed Public Health, Systematic Reviews, and Meta-analysis.- 7. Meta-analysis Methods and Empirical Comparison of Aggregate Data and Individual Participant Data Results from Sample Survey Data.- 8. Statistical Meta-analysis and its Efficience Between Summary Statistics and Individual Participant-level Data: A Monte-Carlo simulation study.- 9. Multivariate Disease Mapping for Multiple Health Outcomes.- 10. Measuring Spatial Dependence of Non-communicable Diseases in South Africa.- 11. Mapping Health Outcomes in Sub-Saharan African Region Using Survey Data, Adjusting for Survey Data - Sheyla Rodrigues Cassy.- 12. Spatial Multi-criteria Decision Analysis in Health Sciences: Fifteen years of applications and trends.- 13. Estimating Determinants of Stage at Diagnosis of Breast Cancer Prevalence in Western Nigeria Using Bayesian Logistic Regression.- 14. Dynamic Bayesian Adjustment of Educational Gradients in Divorce Risks: Disentangling causation and misclassification.- 15. Bayesian Dynamic Models for Time-Varying Outcomes: Applications to a patient cohort on ART.- 16. Suicide Ideation and Associated Factors Among School-going Adolescents in Namibia: A Multilevel logistic regression.- 17. Bayesian Inference in the Extended Generalized Gamma Model and its Special Cases: With applications on demographic and health survey data from Sub-Saharan Africa.- 18. Changing Effects of Covariates on Childhood Mortality in Sub-Saharan Africa: A dynamic Bayesian survival modeling approach.- 19. Group Outliers and Influence Assessments in Clustered Survival Data Modeling.- 20. Joint Modeling of Competing Risks Survival and Longitudinal Data
£131.50
Springer International Publishing AG Advances in Artificial Intelligence – IBERAMIA 2022: 17th Ibero-American Conference on AI, Cartagena de Indias, Colombia, November 23–25, 2022, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 17th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2022, held in Cartagena de Indias, Colombia, in November 2022. The 33 full and 4 short papers presented were carefully reviewed and selected from 67 submissions. The papers are organized in the following topical sections: applications of AI; ethics and smart city; green and sustainable AI; machine learning; natural language processing; robotics and computer vision; simulation and forecasting.Table of ContentsApplications of AI.- Ethics and Smart City.- Green and Sustainable AI.- Machine Learning.- Natural Language Processing.- Robotics and Computer Vision.- Simulation and Forecasting.
£58.49
Springer Exercise Book of Statistical Inference
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£71.99
Springer Statistics for Composite Indicators
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£104.49
De Gruyter Chaos and Chance: An Introduction to Stochastic Aspects of Dynamics
With emphasis on stochastic aspects of deterministic systems this short book introduces the reader to the basic facts and some special topics of applied ergodic theory. It adresses advanced undergraduate and graduate students from various disciplines, i.e. mathematicians, physicists, electrical and mechanical engineers. Based upon a sound (but non-technical) mathematical introduction, a number of typical examples from applications (mostly from mechanics) are thoroughly discussed. By studying both probabilistic and deterministic features of dynamical systems the reader will develop what might be considered a unified view on chaos and chance as two sides of the same thing.
£32.85
Springer International Publishing AG Lévy Matters III: Lévy-Type Processes: Construction, Approximation and Sample Path Properties
Book SynopsisThis volume presents recent developments in the area of Lévy-type processes and more general stochastic processes that behave locally like a Lévy process. Although written in a survey style, quite a few results are extensions of known theorems, and others are completely new. The focus is on the symbol of a Lévy-type process: a non-random function which is a counterpart of the characteristic exponent of a Lévy process. The class of stochastic processes which can be associated with a symbol is characterized, various schemes constructing a stochastic process from a given symbol are discussed, and it is shown how one can use the symbol in order to describe the sample path properties of the underlying process. Lastly, the symbol is used to approximate and simulate Levy-type processes.This is the third volume in a subseries of the Lecture Notes in Mathematics called Lévy Matters. Each volume describes a number of important topics in the theory or applications of Lévy processes and pays tribute to the state of the art of this rapidly evolving subject with special emphasis on the non-Brownian world.Table of ContentsA Primer on Feller Semigroups and Feller Processes.- Feller Generators and Symbols.- Construction of Feller Processes.- Transformations of Feller Processes.- Sample Path Properties.- Global Properties.- Approximation.- Open Problems.- References.- Index.
£36.89
Springer International Publishing AG Random Walks on Disordered Media and their Scaling Limits: École d'Été de Probabilités de Saint-Flour XL - 2010
Book SynopsisIn these lecture notes, we will analyze the behavior of random walk on disordered media by means of both probabilistic and analytic methods, and will study the scaling limits. We will focus on the discrete potential theory and how the theory is effectively used in the analysis of disordered media. The first few chapters of the notes can be used as an introduction to discrete potential theory.Recently, there has been significant progress on the theory of random walk on disordered media such as fractals and random media. Random walk on a percolation cluster(‘the ant in the labyrinth’)is one of the typical examples. In 1986, H. Kesten showed the anomalous behavior of a random walk on a percolation cluster at critical probability. Partly motivated by this work, analysis and diffusion processes on fractals have been developed since the late eighties. As a result, various new methods have been produced to estimate heat kernels on disordered media. These developments are summarized in the notes.Table of ContentsIntroduction.- Weighted graphs and the associated Markov chains.- Heat kernel estimates – General theory.- Heat kernel estimates using effective resistance.- Heat kernel estimates for random weighted graphs.- Alexander-Orbach conjecture holds when two-point functions behave nicely.- Further results for random walk on IIC.- Random conductance model.
£29.69
Springer International Publishing AG Brownian Motion and its Applications to Mathematical Analysis: École d'Été de Probabilités de Saint-Flour XLIII – 2013
Book SynopsisThese lecture notes provide an introduction to the applications of Brownian motion to analysis and more generally, connections between Brownian motion and analysis. Brownian motion is a well-suited model for a wide range of real random phenomena, from chaotic oscillations of microscopic objects, such as flower pollen in water, to stock market fluctuations. It is also a purely abstract mathematical tool which can be used to prove theorems in "deterministic" fields of mathematics.The notes include a brief review of Brownian motion and a section on probabilistic proofs of classical theorems in analysis. The bulk of the notes are devoted to recent (post-1990) applications of stochastic analysis to Neumann eigenfunctions, Neumann heat kernel and the heat equation in time-dependent domains.Table of Contents1. Brownian motion.- 2. Probabilistic proofs of classical theorems.- 3. Overview of the "hot spots" problem.- 4. Neumann eigenfunctions and eigenvalues.- 5. Synchronous and mirror couplings.- 6. Parabolic boundary Harnack principle.- 7. Scaling coupling.- 8. Nodal lines.- 9. Neumann heat kernel monotonicity.- 10. Reflected Brownian motion in time dependent domains.
£999.99
Springer International Publishing AG Handbook of Uncertainty Quantification
Book SynopsisThe topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the promise of achieving risk mitigation through scientific prediction. It has led to the integration of ideas from mathematics, statistics and engineering being used to lend credence to predictive assessments of risk but also to design actions (by engineers, scientists and investors) that are consistent with risk aversion. The objective of this Handbook is to facilitate the dissemination of the forefront of UQ ideas to their audiences. We recognize that these audiences are varied, with interests ranging from theory to application, and from research to development and even execution. Trade Review Table of Contents
£999.99
Springer International Publishing AG Stochastic Population and Epidemic Models: Persistence and Extinction
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£999.99
Springer International Publishing AG Introduction to Uncertainty Quantification
Book SynopsisThis text provides a framework in which the main objectives of the field of uncertainty quantification (UQ) are defined and an overview of the range of mathematical methods by which they can be achieved. Complete with exercises throughout, the book will equip readers with both theoretical understanding and practical experience of the key mathematical and algorithmic tools underlying the treatment of uncertainty in modern applied mathematics. Students and readers alike are encouraged to apply the mathematical methods discussed in this book to their own favorite problems to understand their strengths and weaknesses, also making the text suitable for a self-study.Uncertainty quantification is a topic of increasing practical importance at the intersection of applied mathematics, statistics, computation and numerous application areas in science and engineering. This text is designed as an introduction to UQ for senior undergraduate and graduate students with a mathematical or statistical background and also for researchers from the mathematical sciences or from applications areas who are interested in the field.T. J. Sullivan was Warwick Zeeman Lecturer at the Mathematics Institute of the University of Warwick, United Kingdom, from 2012 to 2015. Since 2015, he is Junior Professor of Applied Mathematics at the Free University of Berlin, Germany, with specialism in Uncertainty and Risk Quantification.Trade Review“Book is one of very few that discuss a vast array of topics in the developing field of uncertainty quantification (UQ). … The text is mathematically rigorous, and though the intended audience is the senior undergraduate or early graduate mathematics student … . this is a book I might recommend to students as a reference for topics related to UQ ... . Overall, this introduction to UQ leaves something to be desired. It is well written … .” (Talea L. Mayo, SIAM Review, Vol. 59 (1), March, 2017)“This book presents a collection of mathematical results related to Uncertainly Quantification (UQ). It is intended as a textbook for senior undergraduate or graduate students with a background in mathematics and statistics. … The book might be suitable for a research seminar where students are exposed for the first time to the mathematics behind UQ.” (Elisabeth Ullmann, Mathematical Reviews, February, 2017)“This book aims to provide an introduction to the mathematics of the quantification of uncertainty. It is intended for students in mathematics and statistics. In the US this would be a graduate level textbook.” (William J. Satzer, MAA Reviews, maa.org, February, 2016)Table of ContentsIntroduction.- Measure and Probability Theory.- Banach and Hilbert Spaces.- Optimization Theory.- Measures of Information and Uncertainty.- Bayesian Inverse Problems.- Filtering and Data Assimilation.- Orthogonal Polynomials and Applications.- Numerical Integration.- Sensitivity Analysis and Model Reduction.- Spectral Expansions.- Stochastic Galerkin Methods.- Non-Intrusive Methods.- Distributional Uncertainty.- References.- Index.
£67.49
Springer International Publishing AG Time Series Econometrics
Book SynopsisThis text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and the basic properties of covariance, investigating the structure and estimation of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series, highlighting its consequences for modeling and forecasting and presenting standard statistical tests and regressions. Next, the text discusses volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic (GARCH) models. The second part of the text devoted to multivariate processes, such as vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models, which have become the main tools in empirical macroeconomics. The text concludes with a discussion of co-integrated models and the Kalman Filter, which is being used with increasing frequency. Mathematically rigorous, yet application-oriented, this self-contained text will help students develop a deeper understanding of theory and better command of the models that are vital to the field. Assuming a basic knowledge of statistics and/or econometrics, this text is best suited for advanced undergraduate and beginning graduate students. Trade Review“Neusser offers an important addition to the market for books on time series econometrics, and definitely fills a gap within the market and complements existing offerings. This is an excellent effort, and I have enjoyed the book.” (Benjamin Wong, Economic Record, Vol. 95 (310), September, 2019)“The present monograph is a practical and comprehensive introduction to an area that lies at the core of econometrics. … It requires minimal prerequisites, and is almost surely accessible to senior undergraduate or beginning graduate students, and certainly to independent researchers … . I find this book to be a valuable addition to the monographic literature on time series.” (Giuseppe Castellacci, Mathematical Reviews, October, 2017)Table of Contents1. Introduction.- 2. ARMA models.- 3. Forecasting stationary processes.- 4. Estimation of Mean and Autocovariance Function.- 5.Estimation of ARMA Models.- 6. Spectral Analysis and Linear Filters.- 7. Integrated Processes.- 8. Models of Volatility.- 9. Multivariate Time series.- 10. Estimation of Covariance Function.- 11. VARMA Processes.- 12. Estimation of VAR Models.- 13. Forecasting with VAR Models.- 14. Interpretation of VAR Models.- 15. Co-integration.- 16. The Kalman Filter.- 17. Appendices.
£999.99
Springer International Publishing AG Mod-ϕ Convergence: Normality Zones and Precise Deviations
Book SynopsisThe canonical way to establish the central limit theorem for i.i.d. random variables is to use characteristic functions and Lévy’s continuity theorem. This monograph focuses on this characteristic function approach and presents a renormalization theory called mod-ϕ convergence. This type of convergence is a relatively new concept with many deep ramifications, and has not previously been published in a single accessible volume. The authors construct an extremely flexible framework using this concept in order to study limit theorems and large deviations for a number of probabilistic models related to classical probability, combinatorics, non-commutative random variables, as well as geometric and number-theoretical objects. Intended for researchers in probability theory, the text is carefully well-written and well-structured, containing a great amount of detail and interesting examples. Trade Review“The book is well written and mathematically rigorous. They authors collect a large variety of results and try to parallel the theory with applications and they do this rather successfully. It may become a standard reference for researchers working on the topic of central limit theorems and large deviation. … this is a useful book for a researcher in probability theory and mathematical statistics. It is very carefully written and collects many new results.” (Nikolai N. Leonenko, zbMATH 1387.60003, 2018)“This beautiful book (together with other publications by these authors) opens a new way of proving limit theorems in probability theory and related areas such as probabilistic number theory, combinatorics, and statistical mechanics. It will be useful to researchers in these and many other areas.” (Zakhar Kabluchko, Mathematical Reviews, September, 2017)Table of ContentsPreface.- Introduction.- Preliminaries.- Fluctuations in the case of lattice distributions.- Fluctuations in the non-lattice case.- An extended deviation result from bounds on cumulants.- A precise version of the Ellis-Gärtner theorem.- Examples with an explicit generating function.- Mod-Gaussian convergence from a factorisation of the PGF.- Dependency graphs and mod-Gaussian convergence.- Subgraph count statistics in Erdös-Rényi random graphs.- Random character values from central measures on partitions.- Bibliography.
£999.99
Springer International Publishing AG Theory and Simulation of Random Phenomena:
Book SynopsisThe purpose of this book is twofold: first, it sets out to equip the reader with a sound understanding of the foundations of probability theory and stochastic processes, offering step-by-step guidance from basic probability theory to advanced topics, such as stochastic differential equations, which typically are presented in textbooks that require a very strong mathematical background. Second, while leading the reader on this journey, it aims to impart the knowledge needed in order to develop algorithms that simulate realistic physical systems. Connections with several fields of pure and applied physics, from quantum mechanics to econophysics, are provided. Furthermore, the inclusion of fully solved exercises will enable the reader to learn quickly and to explore topics not covered in the main text. The book will appeal especially to graduate students wishing to learn how to simulate physical systems and to deepen their knowledge of the mathematical framework, which has very deep connections with modern quantum field theory.Table of Contents1 Review of Probability Theory.- 2 Applications to Mathematical Statistics.- 3 Conditional Probability and Conditional Expectation.- 4 Markov Chains.- 5 Sampling of Random Variables and Simulation.- 6 Brownian Motion.- 7 Introduction to Stochastic Calculus and Ito Integral.- 8 Introduction to Stochastic Differential Equations and Applications.- Bibliography.- Solutions.
£53.99
Springer International Publishing AG Markov Chains
Book SynopsisThis book covers the classical theory of Markov chains on general state-spaces as well as many recent developments. The theoretical results are illustrated by simple examples, many of which are taken from Markov Chain Monte Carlo methods. The book is self-contained, while all the results are carefully and concisely proven. Bibliographical notes are added at the end of each chapter to provide an overview of the literature. Part I lays the foundations of the theory of Markov chain on general states-space. Part II covers the basic theory of irreducible Markov chains on general states-space, relying heavily on regeneration techniques. These two parts can serve as a text on general state-space applied Markov chain theory. Although the choice of topics is quite different from what is usually covered, where most of the emphasis is put on countable state space, a graduate student should be able to read almost all these developments without any mathematical background deeper than that needed to study countable state space (very little measure theory is required). Part III covers advanced topics on the theory of irreducible Markov chains. The emphasis is on geometric and subgeometric convergence rates and also on computable bounds. Some results appeared for a first time in a book and others are original. Part IV are selected topics on Markov chains, covering mostly hot recent developments.Table of ContentsPart I Foundations.- Markov Chains: Basic Definitions.- Examples of Markov Chains.- Stopping Times and the Strong Markov Property.- Martingales, Harmonic Functions and Polsson-Dirichlet Problems.- Ergodic Theory for Markov Chains.- Part II Irreducible Chains: Basics.- Atomic Chains.- Markov Chains on a Discrete State Space.- Convergence of Atomic Markov Chains.- Small Sets, Irreducibility and Aperiodicity.- Transience, Recurrence and Harris Recurrence.- Splitting Construction and Invariant Measures.- Feller and T-kernels.- Part III Irreducible Chains: Advanced Topics.- Rates of Convergence for Atomic Markov Chains.- Geometric Recurrence and Regularity.- Geometric Rates of Convergence.- (f, r)-recurrence and Regularity.- Subgeometric Rates of Convergence.- Uniform and V-geometric Ergodicity by Operator Methods.- Coupling for Irreducible Kernels.- Part IV Selected Topics.- Convergence in the Wasserstein Distance.- Central Limit Theorems.- Spectral Theory.- Concentration Inequalities.- Appendices.- A Notations.- B Topology, Measure, and Probability.- C Weak Convergence.- D Total and V-total Variation Distances.- E Martingales.- F Mixing Coefficients.- G Solutions to Selected Exercises.
£67.49
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Stochastic Differential Equations: An
Book SynopsisThis edition contains detailed solutions of selected exercises. Many readers have requested this, because it makes the book more suitable for self-study. At the same time new exercises (without solutions) have beed added. They have all been placed in the end of each chapter, in order to facilitate the use of this edition together with previous ones. Several errors have been corrected and formulations have been improved. This has been made possible by the valuable comments from (in alphabetical order) Jon Bohlin, Mark Davis, Helge Holden, Patrick Jaillet, Chen Jing, Natalia Koroleva,MarioLefebvre,Alexander Matasov,Thilo Meyer-Brandis, Keigo Osawa, Bjorn Thunestvedt, Jan Uboe and Yngve Williassen. I thank them all for helping to improve the book. My thanks also go to Dina Haraldsson, who once again has performed the typing and drawn the ?gures with great skill. Blindern, September 2002 Bernt Oksendal xv Preface to Corrected Printing, Fifth Edition The main corrections and improvements in this corrected printing are from Chapter 12. I have bene?tted from useful comments from a number of p- ple, including (in alphabetical order) Fredrik Dahl, Simone Deparis, Ulrich Haussmann, Yaozhong Hu, Marianne Huebner, Carl Peter Kirkebo, Ni- lay Kolev, Takashi Kumagai, Shlomo Levental, Geir Magnussen, Anders Oksendal, Jur . . gen Pottho?, Colin Rowat, Stig Sandnes, Lones Smith, S- suo Taniguchi and Bjorn Thunestvedt. I want to thank them all for helping me making the book better. I also want to thank Dina Haraldsson for pro?cient typing.Trade ReviewFrom the reviews of the fifth edition: "This is a highly readable and refreshingly rigorous introduction to stochastic calculus. … This is not a watered-down treatment. It is a serious introduction that starts with fundamental measure-theoretic concepts and ends, coincidentally, with the Black-Scholes formula as one of several examples of applications. This is the best single resource for learning the stochastic calculus … ." (riskbook.com, 2002) From the reviews of the sixth edition: "The book … has evolved from a 200-page typewritten booklet to a modern classic. Part of its charm and success is the fact that the author does not bother too much with the (for the novice) cumbersome rigorous theory … . This does not mean that the book is not rigorous, it is just the timing and dosage of mathematical rigour … that is palatable for undergraduates … . a highly readable account, suitable for self-study and for use in the classroom." (René L. Schilling, The Mathematical Gazette, March, 2005) "This is the sixth edition of the classical and excellent book on stochastic differential equations. The main difference with the next to last edition is the addition of detailed solutions of selected exercises … . This is certainly an excellent idea in view to test its ability of applications of the concepts … . certainly one of the best books on the subject, it will be very helpful to any graduate students and also very valuable for any analysts of financial market." (Stéphane Métens, Physicalia, Vol. 26 (1), 2004) "This is now the sixth edition of the excellent book on stochastic differential equations and related topics. … the presentation is successfully balanced between being easily accessible for a broad audience and being mathematically rigorous. The book is a first choice for courses at graduate level in applied stochastic differential equations. The inclusion of detailed solutions to many of the exercises in this edition also makes it very useful for self-study." (Evelyn Buckwar, Zentralblatt MATH, Vol. 1025, 2003)Table of ContentsSome Mathematical Preliminaries.- Itô Integrals.- The Itô Formula and the Martingale Representation Theorem.- Stochastic Differential Equations.- The Filtering Problem.- Diffusions: Basic Properties.- Other Topics in Diffusion Theory.- Applications to Boundary Value Problems.- Application to Optimal Stopping.- Application to Stochastic Control.- Application to Mathematical Finance.
£47.49
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Probability Essentials
Book SynopsisThis introduction can be used, at the beginning graduate level, for a one-semester course on probability theory or for self-direction without benefit of a formal course; the measure theory needed is developed in the text. It will also be useful for students and teachers in related areas such as finance theory, electrical engineering, and operations research. The text covers the essentials in a directed and lean way with 28 short chapters, and assumes only an undergraduate background in mathematics. Readers are taken right up to a knowledge of the basics of Martingale Theory, and the interested student will be ready to continue with the study of more advanced topics, such as Brownian Motion and Ito Calculus, or Statistical Inference.Trade Review"(The book is) a lean and largely self-contained introduction to the modern theory of probability, aimed at advanced undergraduate or beginning graduate students. The 28 short chapters belie the book's genesis as polished lecture notes; the exposition is sleek and rigorous and each chapter ends with a supporting collection of mainly routine exercises. ... The authors make it clear what luggage is required for this exhilarating trek,... a good knowledge of advanced calculus, some linear algebra, and some "mathematical sophistication". With this understood, the itinerary is immaculately paced and planned with just the right balances of technical ascents and pauses to admire the scenery. Within the constraints of a slim volume, it is hard to imagine how the authors could have done a more effective or more attractive job." The Mathematical Gazette, Vol. 84, No 500, 2000 "The authors provide the shortest path through the twenty-eight chapter headings. The topics are treated in a mathematically and pedagogically digestible way. The writing is concise and crisp: the average chapter length is about eight pages. ... Numerous exercises add to the value of the text as a teaching tool. In conclusion, this is an excellent text for the intended audience."Short Book Reviews, Vol. 21, No. 2, 2001Table of Contents1. Introduction 2. Axioms of Probability 3. Conditional Probability and Independence 4. Probabilities on a Countable Space 5. Random Variables on a Countable Space 6. Construction of a Probability Measure 7. Construction of a Probability Measure on R 8. Random Variables 9. Integration with Respect to a Probability Measure 10. Independent Random Variables 11. Probability Distributions on R 12. Probability Distributions on Rn 13. Characteristic Functions 14. Properties of Characteristic Functions 15. Sums of Independent Random Variables 16. Gaussian Random Variables (The Normal and the Multivariate Normal Distributions) 17. Convergence of Random Variables 18. Weak Convergence 19. Weak Convergence and Characteristic Functions 20. The Laws of Large Numbers 21. The Central Limit Theorem 22. L2 and Hilbert Spaces 23. Conditional Expectation 24. Martingales 25. Supermartingales and Submartingales 26. Martingale Inequalities 27. Martingales Convergence Theorems 28. The Radon-Nikodym Theorem
£52.24
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Stochastic Methods: A Handbook for the Natural
Book SynopsisIn the third edition of this classic the chapter on quantum Marcov processes has been replaced by a chapter on numerical treatment of stochastic differential equations to make the book even more valuable for practitioners.Trade ReviewFrom the reviews of the fourth edition:“This is the fourth edition of a textbook intended for everyone interested in practising stochastic processes. … this fourth one is ‘thoroughly revised and augmented, and has been completely reset. … this new edition is designed to cater better for the wider readership as well as to those [he] originally had in mind’. … The bibliography is well presented, with a list of the references cited in each chapter, a commented global bibliography and an author index.” (Yves Elskens, Belgian Physical Society Magazine, Issue 2, 2012)Table of ContentsA Historical Introduction.- Probability Concepts.- Markov Processes.- The Ito Calculus and Stochastic Differential Equations.- The Fokker-Planck Equation.- The Fokker-Planck Equation in Several Dimensions.- Small Noise Approximations for Diffusion Processes.- The White Noise Limit.- Beyond the White Noise Limit.- Lévy Processes and Financial Applications.- Master Equations and Jump Processes.- The Poisson Representation.- Spatially Distributed Systems.- Bistability, Metastability, and Escape Problems.- Simulation of Stochastic Differential Equations.
£999.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Stochastic Methods: A Handbook for the Natural and Social Sciences
Book SynopsisIn the third edition of this classic the chapter on quantum Marcov processes has been replaced by a chapter on numerical treatment of stochastic differential equations to make the book even more valuable for practitioners.Trade ReviewFrom the reviews of the fourth edition:“This is the fourth edition of a textbook intended for everyone interested in practising stochastic processes. … this fourth one is ‘thoroughly revised and augmented, and has been completely reset. … this new edition is designed to cater better for the wider readership as well as to those [he] originally had in mind’. … The bibliography is well presented, with a list of the references cited in each chapter, a commented global bibliography and an author index.” (Yves Elskens, Belgian Physical Society Magazine, Issue 2, 2012)Table of ContentsA Historical Introduction.- Probability Concepts.- Markov Processes.- The Ito Calculus and Stochastic Differential Equations.- The Fokker-Planck Equation.- The Fokker-Planck Equation in Several Dimensions.- Small Noise Approximations for Diffusion Processes.- The White Noise Limit.- Beyond the White Noise Limit.- Lévy Processes and Financial Applications.- Master Equations and Jump Processes.- The Poisson Representation.- Spatially Distributed Systems.- Bistability, Metastability, and Escape Problems.- Simulation of Stochastic Differential Equations.
£71.24
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Modelling, Pricing, and Hedging Counterparty Credit Exposure: A Technical Guide
Book SynopsisIt was the end of 2005 when our employer, a major European Investment Bank, gave our team the mandate to compute in an accurate way the counterparty credit exposure arising from exotic derivatives traded by the ?rm. As often happens, - posure of products such as, for example, exotic interest-rate, or credit derivatives were modelled under conservative assumptions and credit of?cers were struggling to assess the real risk. We started with a few models written on spreadsheets, t- lored to very speci?c instruments, and soon it became clear that a more systematic approach was needed. So we wrote some tools that could be used for some classes of relatively simple products. A couple of years later we are now in the process of building a system that will be used to trade and hedge counterparty credit ex- sure in an accurate way, for all types of derivative products in all asset classes. We had to overcome problems ranging from modelling in a consistent manner different products booked in different systems and building the appropriate architecture that would allow the computation and pricing of credit exposure for all types of pr- ucts, to ?nding the appropriate management structure across Business, Risk, and IT divisions of the ?rm. In this book we describe some of our experience in modelling counterparty credit exposure, computing credit valuation adjustments, determining appropriate hedges, and building a reliable system.Table of ContentsMethodology.- Modelling Framework.- Simulation Models.- Valuation and Sensitivities.- Architecture and Implementation.- Computational Framework.- Implementation.- Architecture.- Products.- Interest-Rate Products.- Equity, Commodity, Inflation and FX Products.- Credit Derivatives.- Structures.- Hedging and Managing Counterparty Risk.- Counterparty Risk Aggregation and Risk Mitigation.- Combining Market and Credit Risk.- Pricing Counterparty Credit Risk.
£113.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Advances in Markov-Switching Models: Applications in Business Cycle Research and Finance
Book SynopsisThis book is a collection of state-of-the-art papers on the properties of business cycles and financial analysis. The individual contributions cover new advances in Markov-switching models with applications to business cycle research and finance. The introduction surveys the existing methods and new results of the last decade. Individual chapters study features of the U. S. and European business cycles with particular focus on the role of monetary policy, oil shocks and co movements among key variables. The short-run versus long-run consequences of an economic recession are also discussed. Another area that is featured is an extensive analysis of currency crises and the possibility of bubbles or fads in stock prices. A concluding chapter offers useful new results on testing for this kind of regime-switching behaviour. Overall, the book provides a state-of-the-art over view of new directions in methods and results for estimation and inference based on the use of Markov-switching time-series analysis. A special feature of the book is that it includes an illustration of a wide range of applications based on a common methodology. It is expected that the theme of the book will be of particular interest to the macroeconomics readers as well as econometrics professionals, scholars and graduate students. We wish to express our gratitude to the authors for their strong contributions and the reviewers for their assistance and careful attention to detail in their reports.Table of ContentsI Introduction and Overview.- New directions in business cycle research and financial analysis.- II The Business Cycle in the U.S..- Permanent and transitory components of recessions.- Can oil shocks explain asymmetries in the US Business Cycle?.- Markov switching in disaggregate unemployment rates.- III The Business Cycle in Other Countries.- A Markov-switching vector equilibrium correction model of the UK labour market.- Plucking models of business cycle fluctuations: Evidence from the G-7 countries.- IV Financial Applications.- Is there an asymmetric effect of monetary policy over time? A Bayesian analysis using Austrian data.- A regime-switching approach to the study of speculative attacks: A focus on EMS crises.- Fads or bubbles?.- Improving GARCH volatility forecasts with regime-switching GARCH.- V Methodological Contribution.- Power issues when testing the Markov switching model with the sup likelihood ratio test using U.S. Output.- List of Referees.
£80.99
