Böcker i Wiley Series in Probability and Statistics-serien

A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods.

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.

A thorough treatment of the statistical methods used to analyze doubly truncated dataIn The Statistical Analysis of Doubly Truncated Data, an expert team of statisticians delivers an up-to-date review of existing methods used to deal with randomly truncated data, with a focus on the challenging problem of random double truncation. The authors comprehensively introduce doubly truncated data before moving on to discussions of the latest developments in the field.The book offers readers examples with R code along with real data from astronomy, engineering, and the biomedical sciences to illustrate and highlight the methods described within. Linear regression models for doubly truncated responses are provided and the influence of the bandwidth in the performance of kernel-type estimators, as well as guidelines for the selection of the smoothing parameter, are explored.Fully nonparametric and semiparametric estimators are explored and illustrated with real data. R code for reproducing the data examples is also provided. The book also offers:* A thorough introduction to the existing methods that deal with randomly truncated data* Comprehensive explorations of linear regression models for doubly truncated responses* Practical discussions of the influence of bandwidth in the performance of kernel-type estimators and guidelines for the selection of the smoothing parameter* In-depth examinations of nonparametric and semiparametric estimatorsPerfect for statistical professionals with some background in mathematical statistics, biostatisticians, and mathematicians with an interest in survival analysis and epidemiology, The Statistical Analysis of Doubly Truncated Data is also an invaluable addition to the libraries of biomedical scientists and practitioners, as well as postgraduate students studying survival analysis.

This book provides a comprehensive and rigorous treatment of real-life scientific problems which encounter non-linear data. The authors first present methods for developing distributions on a circle. Then, they proceed to show how such methods are generalized for other manifolds. They also consider new methods peculiar to certain other manifolds, like disc and hyperdisc. The organization of the book develops the methods from the beginning for a simple manifold, letting the reader appreciate how these unfold and generalize to more complicated manifolds. Next, rather than separately treating one distribution at a time, the authors develop the generalizations of the methods of derivations. Finally, new distributions are presented as outcomes of these generalizations. The authors also provide several real-life examples, which not only attest to the ongoing usefulness, but will also help the reader visualize other modern day areas of the applications of these important distributions.

This book introduces statistical planning and inference, presenting both classical theory and the major developments in the field. Each chapter presents problems and their solutions along with illustrative examples to introduce concepts and methods, and is supported by a supplementary website featuring guidance on how to implement methods using R

This book will not only be readily welcomed in the academic study of classical optimal selection problems but will also make an impact in areas of modern finance, portfolio management and discrete Mathematics. The initial problem, that of "the classical secretary" dealt with simple and generalised competitive rank problems.

Covering a growing area of research, Spatial Analysis highlights the latest advances in the field with an emphasis on applications. Written by world-renowned authors, this breakthrough text provides insight into the statistical investigation of the interdependence of random variables as a function of their proximity in space and time.

Filling the need for a comprehensive guide on the subject, Applied Time Series Analysis for the Social Sciences presents time series analysis in an accessible format designed to appeal to students and professional researchers with little mathematical and statistical background.

Handbook and reference for industrial statisticians and system reliability engineersSystem Reliability Theory: Models, Statistical Methods, and Applications, Third Edition presents an updated and revised look at system reliability theory, modeling, and analytical methods. The new edition is based on feedback to the second edition from numerous students, professors, researchers, and industries around the world. New sections and chapters are added together with new real-world industry examples, and standards and problems are revised and updated.System Reliability Theory covers a broad and deep array of system reliability topics, including:* In depth discussion of failures and failure modes* The main system reliability assessment methods* Common-cause failure modeling* Deterioration modeling* Maintenance modeling and assessment using Python code* Bayesian probability and methods* Life data analysis using RPerfect for undergraduate and graduate students taking courses in reliability engineering, this book also serves as a reference and resource for practicing statisticians and engineers.Throughout, the book has a practical focus, incorporating industry feedback and real-world industry problems and examples.

With a focus on models and tangible applications of probability from engineering, business, and other related disciplines, this book successfully guides readers through the fundamentals of the subject helping them achieve an increased mathematical sophistication.

Advances in Longitudinal Survey MethodologyExplore an up-to-date overview of best practices in the implementation of longitudinal surveys from leading experts in the field of survey methodologyAdvances in Longitudinal Survey Methodology delivers a thorough review of the most current knowledge in the implementation of longitudinal surveys. The book provides a comprehensive overview of the many advances that have been made in the field of longitudinal survey methodology over the past fifteen years, as well as extending the topic coverage of the earlier volume, "Methodology of Longitudinal Surveys", published in 2009. This new edited volume covers subjects like dependent interviewing, interviewer effects, panel conditioning, rotation group bias, measurement of cognition, and weighting.New chapters discussing the recent shift to mixed-mode data collection and obtaining respondents' consent to data linkage add to the book's relevance to students and social scientists seeking to understand modern challenges facing data collectors today. Readers will also benefit from the inclusion of:* A thorough introduction to refreshment sampling for longitudinal surveys, including consideration of principles, sampling frame, sample design, questionnaire design, and frequency* An exploration of the collection of biomarker data in longitudinal surveys, including detailed measurements of ill health, biological pathways, and genetics in longitudinal studies* An examination of innovations in participant engagement and tracking in longitudinal surveys, including current practices and new evidence on internet and social media for participant engagement.An invaluable source for post-graduate students, professors, and researchers in the field of survey methodology, Advances in Longitudinal Survey Methodology will also earn a place in the libraries of anyone who regularly works with or conducts longitudinal surveys and requires a one-stop reference for the latest developments and findings in the field.

Praise for the Fourth Edition The book follows faithfully the style of the original edition. The approach is heavily motivated by real-world time series, and by developing a complete approach to model building, estimation, forecasting and control.

Applied Econometric Time Series, 4th Edition demonstrates modern techniques for developing models capable of forecasting, interpreting, and testing hypotheses concerning economic data. In this text, Dr.

Basic and Advanced Structural Equation Models for Medical and Behavioural Sciences introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject's recent advances.

Bayesian Analysis of Stochastic Process Models provides analysis of stochastic processes from a Bayesian perspective with coverage of the main classes of stochastic processing, including modeling, computational, inference, prediction, decision-making, and important applied models based on stochastic processes.

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.

* 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.