Böcker i Wiley Series in Probability and Statistics-serien

Since the publication of the first edition, the authors have solicited feedback from both the instructors who use the book as a text for their courses as well as the researchers who use the book as a resource for their research.

Statistical Intervals is a guide for practitioners and researchers--providing a detailed, comprehensive, modernized treatment of this important subject. With numerous examples, it presents and differentiates in an easy-to-apply manner the use of confidence intervals (e.g.

Understand and utilize the latest developments in Weibull inferential methods While the Weibull distribution is widely used in science and engineering, most engineers do not have the necessary statistical training to implement the methodology effectively.

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 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.

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.

PRICING INSURANCE RISKA comprehensive framework for measuring, valuing, and managing riskPricing Insurance Risk: Theory and Practice delivers an accessible and authoritative account of how to determine the premium for a portfolio of non-hedgeable insurance risks and how to allocate it fairly to each portfolio component.The authors synthesize hundreds of academic research papers, bringing to light little-appreciated answers to fundamental questions about the relationships between insurance risk, capital, and premium. They lean on their industry experience throughout to connect the theory to real-world practice, such as assessing the performance of business units, evaluating risk transfer options, and optimizing portfolio mix.Readers will discover:* Definitions, classifications, and specifications of risk* An in-depth treatment of classical risk measures and premium calculation principles* Properties of risk measures and their visualization* A logical framework for spectral and coherent risk measures* How risk measures for capital and pricing are distinct but interact* Why the cost of capital, not capital itself, should be allocated* The natural allocation method and how it unifies marginal and risk-adjusted probability approaches* Applications to reserve risk, reinsurance, asset risk, franchise value, and portfolio optimizationPerfect for actuaries working in the non-life or general insurance and reinsurance sectors, Pricing Insurance Risk: Theory and Practice is also an indispensable resource for banking and finance professionals, as well as risk management professionals seeking insight into measuring the value of their efforts to mitigate, transfer, or bear nonsystematic risk.

This 1971 classic on linear models features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.

Geostatistical Functional Data AnalysisExplore the intersection between geostatistics and functional data analysis with this insightful new referenceGeostatistical Functional Data Analysis presents a unified approach to modelling functional data when spatial and spatio-temporal correlations are present. The Editors link together the wide research areas of geostatistics and functional data analysis to provide the reader with a new area called geostatistical functional data analysis that will bring new insights and new open questions to researchers coming from both scientific fields. This book provides a complete and up-to-date account to deal with functional data that is spatially correlated, but also includes the most innovative developments in different open avenues in this field.Containing contributions from leading experts in the field, this practical guide provides readers with the necessary tools to employ and adapt classic statistical techniques to handle spatial regression. The book also includes:* A thorough introduction to the spatial kriging methodology when working with functions* A detailed exposition of more classical statistical techniques adapted to the functional case and extended to handle spatial correlations* Practical discussions of ANOVA, regression, and clustering methods to explore spatial correlation in a collection of curves sampled in a region* In-depth explorations of the similarities and differences between spatio-temporal data analysis and functional data analysisAimed at mathematicians, statisticians, postgraduate students, and researchers involved in the analysis of functional and spatial data, Geostatistical Functional Data Analysis will also prove to be a powerful addition to the libraries of geoscientists, environmental scientists, and economists seeking insightful new knowledge and questions at the interface of geostatistics and functional data analysis.

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.

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 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.

The importance of nonparametric methods in modern statistics has grown dramatically since their inception in the mid-1930s. Requiring few or no assumptions about the populations from which data are obtained, they have emerged as the preferred methodology among statisticians and researchers performing data analysis.

A highly detailed, yet readable treatment of the growing field of robust statistics--the statistics of approximate parametric models. Introducing concepts, theory, and applications, this work is designed to be accessible to a broad audience, avoiding allusions to high-powered mathematics while emphasizing ideas, heuristics, and background.

Bayesian statistics uses information from past experience to infer the results of future events. With recent advances in computing power and the development of computer intensive methods for statistical estimation, Bayesian approaches to model estimation have become more feasible and popular.

A reference guide for applications of SEM using Mplus Structural Equation Modeling: Applications Using Mplus is intended as both a teaching resource and a reference guide. Written in non-mathematical terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM).

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.

This book provides a lucid and comprehensive introduction to simulation methods, and features examples and applications using Maple. Maple is widely used at undergraduate mathematics level in the UK, US and Europe. It is readily available to students, researchers and practitioners, and the code is easily transferrable into other languages.

- It reveals the interrelationships between multiple variables and features of the underlying conditional independence. - It covers conditional independence, several types of independence graphs, Gaussian models, issues in model selection, regression and decomposition. - Many numerical examples and exercises with solutions are included.

Presents methods for the design and analysis of surveys, studies, and experiments when the data is qualitative and categorical. This work also covers the delta methods for multinomial frequencies. It discusses topics in misclassification and in reliability assessment.

An essential introductory text linking traditional biostatistics with bayesian methods In recent years, Bayesian methods have seen an explosion of interest, with applications in fields including biochemistry, ecology, medicine, oncology, pharmacology, and public health.

This text provides a source for professionals in the insurance industry who have a modest level of mathematical experience. It outlines classical results and provides an insight into recent developments in applied probability theory illustrating relevant applications in insurance mathematics.

Designed to serve as a reference for the practitioner and as a self contained textbook for the advanced student.

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.

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.