Books in the Wiley Series in Probability and Statistics series

Written by authorities in the field, Lower Previsions illustrates how the theory of Lower Previsions can be extended to cover a larger set of random quantities. The text highlights a crucial problem in the theory of imprecise probability and provides a detailed theory on how to resolve it.

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.

Times Series Analysis and Forecasting presents seemingly difficult techniques and methodologies in an insightful and application-based way. Through a hands-on and user-friendly approach, this text includes exercises, graphical techniques, examples, excel spreadsheets, and software applications on time series analysis.

* Serves as a fundamental introduction to statistical learning theory and its role in understanding human learning and inductive reasoning. * Topics of coverage include: probability, pattern recognition, optimal Bayes decision rule, nearest neighbor rule, kernel rules, neural networks, and support vector machines.

* This is the first book of its kind on the subject. It is written by experts in the field (such as J.D. Williams of General Electric Global Research, Jeffrey B. Birch at VPI, and Longcheen Huwang of the Institute of Statistics at Tsing Hua University Hsin Chu). It is current and presents state-of-the-art materials.

Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples.

* Detailed descriptions of assumptions, the consequences of violating assumptions, and alternative procedures to follow are explained throughout the book.

This book explores the historical and philosophical implications inherent in any study of statistical data analysis. It addresses the needs of researchers who are working with larger, complicated data sets by offering an understanding of the significance of robust data sets, the implementation of software languages, and the use of models.

This second edition of one of the best-selling books on geostatistics provides through updates from two authoritative authors with over twenty years of experience in the field. It removes information and data that have lost relevance with time while maintaining timeless, core methods and integrating them with new developments to the field.

* First Edition users testify that the book is well written and expertly documented. * The reader is introduced to provocative pointers such as the relevance of graphs, the meaning of interpretation, Henry Ford s Code of Practice, and Deming s 14 points, among others.

Providing a thorough treatment on statistical causality, this resource presents a broad collection of contributions from experts in their fields. Methods and their applications are provided with theoretical background and emphasis is given to practice rather than theory, with technical content kept to a minimum.

This third volume in the series discusses special modifications and extensions of designs that arise in certain fields of application such as genetics, bioinformatics, agriculture, medicine, manufacturing, marketing, and more. The book is written by an expert panel of contributors.

This volume addresses a concern of very high relevance and growing interest for large industries or environmentalists: risk and uncertainty in complex systems.

The concise yet authoritative presentation of key techniques for basic mixtures experiments Inspired by the author's bestselling advanced book on the topic, A Primer on Experiments with Mixtures provides an introductory presentation of the key principles behind experimenting with mixtures.

?? Provides a concise but rigorous account of the theoretical background of FDA. ?? Introduces topics in various areas of mathematics, probability and statistics from the perspective of FDA. ?? Presents a systematic exposition of the fundamental statistical issues in FDA.

Introducing a groundbreaking companion book to a bestselling reliability text Reliability is one of the most important characteristics defining the quality of a product or system, both for the manufacturer and the purchaser.

An essential resource for constructing and analyzing advanced actuarial models Loss Models: Further Topics presents extended coverage of modeling through the use of tools related to risk theory, loss distributions, and survival models.

This new edition, now with a co-author, offers a complete and up-to-date examination of the field. The authors have streamlined previously tedious topics, such as multivariate regression and MANOVA techniques, to add newer, more timely content.

A comprehensive presentation of control theory for the SPC community Quality control has become a major concern in today's competitive industrial environment, and industrial engineers are constantly seeking to make process adjustments that will optimize production efficiency and improve product quality.

* The first book to chart the development and applications of this growing subject. * Provides a comprehensive introduction to imprecise probabilities, including theory and applications reflecting the current state of the art. * Each chapter is written by leading experts in their field.

Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences.

Praise for the First Edition This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one s personal library.

Presents a useful new technique for analyzing the extreme-value behaviour of random fields Modern science typically involves the analysis of increasingly complex data. The extreme values that emerge in the statistical analysis of complex data are often of particular interest.

A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods.

* This book focuses on the comparison, contrast, and assessment of risks on the basis of clinical investigations. * It develops basic concepts as well as deriving biostatistical methods through both the application of classical mathematical statistical tools and more modern likelihood-based theories.

Multiple Comparison Procedures offers an up-to-date view of multiple comparison procedures, disproving the belief held by some statisticians that such procedures have no place in data analysis.