Böcker i Wiley Series in Probability and Statistics-serien

In the spatial or spatio-temporal context, specifying the correct covariance function is fundamental to obtain efficient predictions, and to understand the underlying physical process of interest. This book focuses on covariance and variogram functions, their role in prediction, and appropriate choice of these functions in applications.

Throughout the social, medical and other sciences the importance of understanding complex hierarchical data structures is well understood. Multilevel modelling is now the accepted statistical technique for handling such data and is widely available in computer software packages.

* This book lays out in clear detail the various but subtle threats (commonly called biases) to various validities of statistical research. * There is a comprehensive discussion of the sources of bias in comparative studies (both randomized and observational) and how to address them.

Upgraded to reflect the latest research and software applications on the topic, this new edition continues to provide a comprehensive introduction to the statistical methods for analyzing survival data.

This introduction to elementary probability theory is intended to serve as a pocketbook for applied statisticians. Topics include random walks; the principle of reflection; the probabilistic aspects of records; the geometric distribution; optimization and others.

Emphasizes the strategy of experimentation, data analysis, and the interpretation of experimental results. * Features numerous examples using actual engineering and scientific studies. * Presents statistics as an integral component of experimentation from the planning stage to the presentation of the conclusions.

An accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series.

While most books on reliability deal with a description of component and system states as binary, i.e. , functioning or failed, many systems are composed of multistate components with different performance levels and several failure modes. This book addresses the need in a number of applications for a more refined description of these states.

This new edition answers the need for a comprehensive, cutting-edge overview of this important and emerging field effectively outlining all phases of this revolutionary analytical technique, from preprocessing to the analysis stage.

A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applications More and more of today s numerical problems found in engineering and finance are solved through Monte Carlo methods.

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.

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.