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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.

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.

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

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.

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.

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.

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.

- 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.

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.

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.

Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over more than a quarter of a century ago.

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.

Statistical Methods for Spatial and Spatio-Temporal Data Analysis provides a complete range of spatio-temporal covariance functions and discusses ways of constructing them. This book is a unified approach to modeling spatial and spatio-temporal data together with significant developments in statistical methodology with applications in R.

Written to convey an intuitive feeling for both theory and practice, this book illustrates what a powerful tool density estimation can be when used not only with univariate and bivariate data but also in the higher dimensions of trivariate and quadrivariate information.

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.

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.

This book combines the authors' experiences on the topic and brings out a wealth of new information relevant to the study of meta-analysis. Applications ranging from business to education to environment to health sciences in both univariate and multivariate cases are presented alongside and subservient to theory.

Winner of the 2008 Ziegel Prize for outstanding new book of the year Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account.

A review of the state of the art in smart antennas and document in an easy-to-understand format, coverage includes the analysis and design issues crucial for a successful design. It provides the reader with tools to enhance the development cycle by tying the theory and practice together, producing superior results.

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.

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.

* 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.