Bayesian Forecasting and Dynamic Models / Edition 2

by Mike West, Jeff Harrison

Overview

The second edition of this book includes revised, updated, and additional material on the structure, theory, and application of classes of dynamic models in Bayesian time series analysis and forecasting. In addition to wide ranging updates to central material, the second edition includes many more exercises and covers new topics at the research and application frontiers of Bayesian forecastings.

Product Details

ISBN-13:	9780387947259
Publisher:	Springer New York
Publication date:	01/24/1997
Series:	Springer Series in Statistics Series
Edition description:	2nd ed. 1997. Corr. 2nd printing 1999
Pages:	682
Product dimensions:	6.14(w) x 9.21(h) x 0.36(d)

Table of Contents

Introduction.- Introduction to the DLM: The first-order polynomial model.- Introduction to the DLM: The regression DLM.- The Dynamic Linear Model.- Univariate Time Series DLM Theory.- Model Specification and Design.- Polynomial Trend Models.- Seasonal Models.- Regression, Autoregression, and Related Models.- Illustrations and Extensions of Standard DLMS.- Intervention and Monitoring.- Multi-Process Models.- Non-Linear Dynamic Models: Analytic and Numerical Approximations.- Exponential Family Dynamic Models.- Simulation-Based Methods in Dynamic Models.- Multivariate Modelling and Forecasting.- Distribution Theory and Linear Algebra. Bibliography.- Author Index.- Subject Index.

Editorial Reviews

Text provides graduate students of statistics (and professionals in the field) with a carefully detailed account of techniques (many of recent invention) which pertain to forecasting in time-dependent contexts. Sixteen chapters, many examples and exercises. Exceptionally lucid, if somewhat intimidating in its bulk. (NW) Annotation c. Book News, Inc., Portland, OR (booknews.com)

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