New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: <ul>
<li>Contributions from eleven of the worldâ??s leading figures in time series</li> <li>Shared balance between theory and application</li> <li>Exercise series sets</li> <li>Many real data examples</li> <li>Consistent style and clear, common notation in all contributions</li> <li>60 helpful graphs and tables</li> </ul>
Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor's Manual presenting detailed solutions to all the problems in he book is available upon request from the Wiley editorial department.

A Course in Time Series Analysis

Science & Math

Tsay, Ruey S.

Tiao, George C.

Peña, Daniel

Mathematics

Wiley-Interscience

Bayesian Inference in Statistical Analysis

Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.

Box, George E. P.

Optimal Statistical DecisionsThis book provides a thorough discussion in the theory and methodology of optimal statistical decisions. The volume represents a landmark. It still remains the clearest introduction to Bayesian statistical decision theory even in, this, a paperback reprint of the original book dating back to 1970 (under the aegis of McGraw Hill, Inc.). While the content does not include the computational advances that have become so popular and well used today, neither does it neglect what purposes are served by those computations. DeGroot’s book, with its clear exposition of Bayesian principles, is useful to keep those purposes in mind. Of particular note is the fact that Bayesian and sequential decision problems are explained from the bottom-up with great care and clarity. Bayesian Inference in Statistical AnalysisIts main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach. Applied Statistical Decision Theory"In the field of statistical decision theory, Raiffa and Schlaifer have sought to develop new analytic techniques by which the modern theory of utility and subjective probability can actually be applied to the economic analysis of typical sampling problems."—From the foreword to their classic work Applied Statistical Decision Theory. First published in the 1960s through Harvard University and MIT Press, the book is now offered in a new paperback edition from Wiley

Optimal Statistical Decision & Bayesian Inference in Statistical Analysis & Applied Statistical Decision Theory

DeGroot, Morris H.

Raiffa, Howard

Schlaifer, Robert

wiley

This is a special, one-time volume, commemorating the 80th birthday of George E. P. Box. It contains over 75 articles on quality and quality-related issues, written by Box himself since 1982. Previous to this book, the editors published a similar volume on Box' writings up to 1982; it has since become a standard and useful reference for students and practitioners alike in the field of statistics.

Box on Quality and Discovery: with Design, Control, and Robustness (Wiley Series in Probability and Statistics)

Nature & Ecology

Collected Works of George EP Box, Volume I (Wadsworth Statistics/Probability Series)

Chapman and Hall/CRC

This book should be of interest to professional statisticians, libraries and researchers.

The Collected Works Of George E.P. Box, Volume II (Wadsworth Statistics/Probability Series)

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