Bøger i Wiley Series in Probability and Statistics serien

Comprehensive Reference Work on Multivariate Analysis and Its ApplicationsThe first edition of this book, by Mardia, Kent and Bibby, has been widely used globally for over 40 years. This second edition brings many topics up to date, with a special emphasis on recent developments.A wide range of material in multivariate analysis is covered, including the classical themes of multivariate normal theory, multivariate regression, inference, multidimensional scaling, factoranalysis, cluster analysis and principal component analysis. The book also now covers modern developments such as graphical models, robust estimation, statistical learning, and high-dimensional methods. The book expertly blends theory and application, providing numerous worked examples and exercises at the end of each chapter. The reader is assumed to have a basic knowledge of mathematical statistics at an undergraduate level together with an elementary understanding of linear algebra. There are appendices which provide a background in matrix algebra, a summary of univariate statistics, a collection of statistical tables and a discussion of computational aspects. The work includes coverage of:* Basic properties of random vectors, normal distribution theory, and estimation* Hypothesis testing, multivariate regression, and analysis of variance* Principal component analysis, factor analysis, and canonical correlation analysis* Cluster analysis and multidimensional scaling* New advances and techniques, including statistical learning, graphical models and regularization methods for high-dimensional dataAlthough primarily designed as a textbook for final year undergraduates and postgraduate students in mathematics and statistics, the book will also be of interest to research workers and applied scientists.

Provides a foundation in classical parametric methods of regression and classification essential for pursuing advanced topics in predictive analytics and statistical learningThis book covers a broad range of topics in parametric regression and classification including multiple regression, logistic regression (binary and multinomial), discriminant analysis, Bayesian classification, generalized linear models and Cox regression for survival data. The book also gives brief introductions to some modern computer-intensive methods such as classification and regression trees (CART), neural networks and support vector machines.The book is organized so that it can be used by both advanced undergraduate or masters students with applied interests and by doctoral students who also want to learn the underlying theory. This is done by devoting the main body of the text of each chapter with basic statistical methodology illustrated by real data examples. Derivations, proofs and extensions are relegated to the Technical Notes section of each chapter, Exercises are also divided into theoretical and applied. Answers to selected exercises are provided. A solution manual is available to instructors who adopt the text.Data sets of moderate to large sizes are used in examples and exercises. They come from a variety of disciplines including business (finance, marketing and sales), economics, education, engineering and sciences (biological, health, physical and social). All data sets are available at the book's web site. Open source software R is used for all data analyses. R codes and outputs are provided for most examples. R codes are also available at the book's web site.Predictive Analytics: Parametric Models for Regression and Classification Using R is ideal for a one-semester upper-level undergraduate and/or beginning level graduate course in regression for students in business, economics, finance, marketing, engineering, and computer science. It is also an excellent resource for practitioners in these fields.

- This new edition presents a unified, accessible, and self-contained treatment of the latest statistical methods for analyzing correlated, non-normally distributed data. - The book's unified treatment addresses the needs of applications-oriented users of statistical packages and also graduate students in statistics.

Statistical methods for quality improvement offer numerous benefits for industry and business, both through identifying existing trouble spots and alerting management and technical personnel to potential problems.

With this comprehensive book, readers will learn how to design and set up mixture experiments and then analyze the data and draw inferences from the results. The third edition incorporates in-depth information from over 73 articles, covering the developments of the past decade.

A comprehensive, must-have handbook of matrix methods with a unique emphasis on statistical applications This timely book, A Matrix Handbook for Statisticians, provides a comprehensive, encyclopedic treatment of matrices as they relate to both statistical concepts and methodologies.

Praise for the Second Edition "A must-have book for anyone expecting to do research and/or applications in categorical data analysis. " Statistics in Medicine "It is a total delight reading this book.

Game-theoretic probability and finance come of ageGlenn Shafer and Vladimir Vovk's Probability and Finance, published in 2001, showed that perfect-information games can be used to define mathematical probability. Based on fifteen years of further research, Game-Theoretic Foundations for Probability and Finance presents a mature view of the foundational role game theory can play. Its account of probability theory opens the way to new methods of prediction and testing and makes many statistical methods more transparent and widely usable. Its contributions to finance theory include purely game-theoretic accounts of Ito's stochastic calculus, the capital asset pricing model, the equity premium, and portfolio theory.Game-Theoretic Foundations for Probability and Finance is a book of research. It is also a teaching resource. Each chapter is supplemented with carefully designed exercises and notes relating the new theory to its historical context.Praise from early readers"Ever since Kolmogorov's Grundbegriffe, the standard mathematical treatment of probability theory has been measure-theoretic. In this ground-breaking work, Shafer and Vovk give a game-theoretic foundation instead. While being just as rigorous, the game-theoretic approach allows for vast and useful generalizations of classical measure-theoretic results, while also giving rise to new, radical ideas for prediction, statistics and mathematical finance without stochastic assumptions. The authors set out their theory in great detail, resulting in what is definitely one of the most important books on the foundations of probability to have appeared in the last few decades." - Peter Grünwald, CWI and University of Leiden"Shafer and Vovk have thoroughly re-written their 2001 book on the game-theoretic foundations for probability and for finance. They have included an account of the tremendous growth that has occurred since, in the game-theoretic and pathwise approaches to stochastic analysis and in their applications to continuous-time finance. This new book will undoubtedly spur a better understanding of the foundations of these very important fields, and we should all be grateful to its authors." - Ioannis Karatzas, Columbia University

It provides an introduction to Bayesian methods, specifically tailored for students of the social sciences. Includes detailed definitions of key Bayesian ideas, assuming little background knowledge. Each chapter contains graded exercises to help further the student's understanding of the methods and applications.

First published by Wiley in 1978, this book is being re-issued with a new Preface by the author. The roots of the book lie in the writings of RA Fisher both as concerns results and the general stance to statistical science, and this stance was the determining factor in the author's selection of topics. His treatise brings together results on aspects of statistical information, notably concerning likelihood functions, plausibility functions, ancillarity, and sufficiency, and on exponential families of probability distributions.

Handbook and reference guide for students and practitioners of statistical regression-based analyses in RHandbook of Regression Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical programming language. The authors' thorough treatment of "classical" regression analysis in the first edition is complemented here by their discussion of more advanced topics including time-to-event survival data and longitudinal and clustered data.The book further pays particular attention to methods that have become prominent in the last few decades as increasingly large data sets have made new techniques and applications possible. These include:* Regularization methods* Smoothing methods* Tree-based methodsIn the new edition of the Handbook, the data analyst's toolkit is explored and expanded. Examples are drawn from a wide variety of real-life applications and data sets. All the utilized R code and data are available via an author-maintained website.Of interest to undergraduate and graduate students taking courses in statistics and regression, the Handbook of Regression Analysis will also be invaluable to practicing data scientists and statisticians.

Praise for the First Edition:"If you ... want an up-to-date, definitive reference written by authors who have contributed much to this field, then this book is an essential addition to your library."--Journal of the American Statistical AssociationA COMPREHENSIVE REVIEW OF MODERN EXPERIMENTAL DESIGNExperiments: Planning, Analysis, and Optimization, Third Edition provides a complete discussion of modern experimental design for product and process improvement--the design and analysis of experiments and their applications for system optimization, robustness, and treatment comparison. While maintaining the same easy-to-follow style as the previous editions, this book continues to present an integrated system of experimental design and analysis that can be applied across various fields of research including engineering, medicine, and the physical sciences. New chapters provide modern updates on practical optimal design and computer experiments, an explanation of computer simulations as an alternative to physical experiments. Each chapter begins with a real-world example of an experiment followed by the methods required to design that type of experiment. The chapters conclude with an application of the methods to the experiment, bridging the gap between theory and practice.The authors modernize accepted methodologies while refining many cutting-edge topics including robust parameter design, analysis of non-normal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays.The third edition includes:* Information on the design and analysis of computer experiments* A discussion of practical optimal design of experiments* An introduction to conditional main effect (CME) analysis and definitive screening designs (DSDs)* New exercise problemsThis book includes valuable exercises and problems, allowing the reader to gauge their progress and retention of the book's subject matter as they complete each chapter.Drawing on examples from their combined years of working with industrial clients, the authors present many cutting-edge topics in a single, easily accessible source. Extensive case studies, including goals, data, and experimental designs, are also included, and the book's data sets can be found on a related FTP site, along with additional supplemental material. Chapter summaries provide a succinct outline of discussed methods, and extensive appendices direct readers to resources for further study.Experiments: Planning, Analysis, and Optimization, Third Edition is an excellent book for design of experiments courses at the upper-undergraduate and graduate levels. It is also a valuable resource for practicing engineers and statisticians.

Master the fundamentals of correspondence analysis with this illuminating resourceAn Introduction to Correspondence Analysis assists researchers in improving their familiarity with the concepts, terminology, and application of several variants of correspondence analysis. The accomplished academics and authors deliver a comprehensive and insightful treatment of the fundamentals of correspondence analysis, including the statistical and visual aspects of the subject.Written in three parts, the book begins by offering readers a description of two variants of correspondence analysis that can be applied to two-way contingency tables for nominal categories of variables. Part Two shifts the discussion to categories of ordinal variables and demonstrates how the ordered structure of these variables can be incorporated into a correspondence analysis. Part Three describes the analysis of multiple nominal categorical variables, including both multiple correspondence analysis and multi-way correspondence analysis.Readers will benefit from explanations of a wide variety of specific topics, for example:* Simple correspondence analysis, including how to reduce multidimensional space, measuring symmetric associations with the Pearson Ratio, constructing low-dimensional displays, and detecting statistically significant points* Non-symmetrical correspondence analysis, including quantifying asymmetric associations* Simple ordinal correspondence analysis, including how to decompose the Pearson Residual for ordinal variables* Multiple correspondence analysis, including crisp coding and the indicator matrix, the Burt Matrix, and stacking* Multi-way correspondence analysis, including symmetric multi-way analysisPerfect for researchers who seek to improve their understanding of key concepts in the graphical analysis of categorical data, An Introduction to Correspondence Analysis will also assist readers already familiar with correspondence analysis who wish to review the theoretical and foundational underpinnings of crucial concepts.

Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive.

A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models.

A guide that provides in-depth coverage of modeling techniques used throughout many branches of actuarial science, revised and updatedNow in its fifth edition, Loss Models: From Data to Decisions puts the focus on material tested in the Society of Actuaries (SOA) newly revised Exams STAM (Short-Term Actuarial Mathematics) and LTAM (Long-Term Actuarial Mathematics). Updated to reflect these exam changes, this vital resource offers actuaries, and those aspiring to the profession, a practical approach to the concepts and techniques needed to succeed in the profession. The techniques are also valuable for anyone who uses loss data to build models for assessing risks of any kind.Loss Models contains a wealth of examples that highlight the real-world applications of the concepts presented, and puts the emphasis on calculations and spreadsheet implementation. With a focus on the loss process, the book reviews the essential quantitative techniques such as random variables, basic distributional quantities, and the recursive method, and discusses techniques for classifying and creating distributions. Parametric, non-parametric, and Bayesian estimation methods are thoroughly covered. In addition, the authors offer practical advice for choosing an appropriate model. This important text:* Presents a revised and updated edition of the classic guide for actuaries that aligns with newly introduced Exams STAM and LTAM* Contains a wealth of exercises taken from previous exams* Includes fresh and additional content related to the material required by the Society of Actuaries (SOA) and the Canadian Institute of Actuaries (CIA)* Offers a solutions manual available for further insight, and all the data sets and supplemental material are posted on a companion siteWritten for students and aspiring actuaries who are preparing to take the SOA examinations, Loss Models offers an essential guide to the concepts and techniques of actuarial science.

Incorporating a large body of new work in the field, this work includes the applications of modern missing data methods to real data. It also examines the theoretical and technical extensions that take advantage of computational advances.

Updated classic statistics text, with new problems and examplesProbability and Statistical Inference, Third Edition helps students grasp essential concepts of statistics and its probabilistic foundations. This book focuses on the development of intuition and understanding in the subject through a wealth of examples illustrating concepts, theorems, and methods. The reader will recognize and fully understand the why and not just the how behind the introduced material.In this Third Edition, the reader will find a new chapter on Bayesian statistics, 70 new problems and an appendix with the supporting R code. This book is suitable for upper-level undergraduates or first-year graduate students studying statistics or related disciplines, such as mathematics or engineering. This Third Edition:* Introduces an all-new chapter on Bayesian statistics and offers thorough explanations of advanced statistics and probability topics* Includes 650 problems and over 400 examples - an excellent resource for the mathematical statistics class sequence in the increasingly popular "flipped classroom" format* Offers students in statistics, mathematics, engineering and related fields a user-friendly resource* Provides practicing professionals valuable insight into statistical toolsProbability and Statistical Inference offers a unique approach to problems that allows the reader to fully integrate the knowledge gained from the text, thus, enhancing a more complete and honest understanding of the topic.

Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resourceStatistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented.Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications.Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like:* New ways to plot large sets of time series* An automatic procedure to build univariate ARMA models for individual components of a large data set* Powerful outlier detection procedures for large sets of related time series* New methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series* Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models* Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series* Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting.* Introduction of modern procedures for modeling and forecasting spatio-temporal dataPerfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.

WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation.

Praise for the Second Edition "An essential desktop reference book... it should definitely be on your bookshelf.

Clinical trials are conducted to allow safety and efficacy data to be collected for health interventions. These trials can only take place once satisfactory information has been gathered on the quality of the non-clinical safety.

Markov Chains: Analytic and Monte Carlo Computations introduces the main notions related to Markov chains and provides explanations on how to characterize, simulate, and recognize them.

An up-to-date version of the complete, self-contained introduction to matrix analysis theory and practice Providing accessible and in-depth coverage of the most common matrix methods now used in statistical applications, Matrix Analysis for Statistics, Third Edition features an easy-to-follow theorem/proof format.

Due to highΓÇôspeed internet and the power and speed of the new generation of computers, a researcher now faces somevery challenging phenomena and must deal with an everΓÇôincreasing amount of data. In order to find useful information and hidden patterns underlying the data, a researcher may use various dataΓÇômining methods and techniques for random samples. Adding a time dimension to these large databases certainly introduces new aspects and challenges. Following on from his highly successful and much lauded book, Time Series AnalysisΓÇôUnivariate and Multivariate Methods, this new work focuses is on high dimensional multivariate time series, illustrated with many high dimensional empirical time series. Multivariate Time Series Analysis and its Applications includes many topics that are not found in general multivariate time series books: repeated measurements space time series modelling dimension reduction This book is designed for an advanced time series analysis course, where researchΓÇôoriented projects will be suggested rather than introductory topics covered. It is a mustΓÇôhave for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering.

Provides a one-stop resource for engineers learning biostatistics using MATLAB and WinBUGS Through its scope and depth of coverage, this book addresses the needs of the vibrant and rapidly growing bio-oriented engineering fields while implementing software packages that are familiar to engineers.

Understanding approximate dynamic programming (ADP) in large industrial settings helps develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty.

* The book is written by a first-class, world-renown authority in probability and measure theory at a leading U.S. institution of higher education * The book has been class-tested at over 200 universities around the globe * Theory is first-and-foremost.

A comprehensive compilation of new developments in data linkage methodology The increasing availability of large administrative databases has led to a dramatic rise in the use of data linkage, yet the standard texts on linkage are still those which describe the seminal work from the 1950-60s, with some updates.