Optimal Statistical Decision & Bayesian Inference in Statistical Analysis & Applied Statistical Decision Theory
deGroot, Morris H., Box, George E. P., Tiao, George C.
- 出版商: Wiley
- 出版日期: 2006-05-19
- 售價: $13,920
- 貴賓價: 9.5 折 $13,224
- 語言: 英文
- 頁數: 1476
- 裝訂: Quality Paper - also called trade paper
- ISBN: 047168788X
- ISBN-13: 9780471687887
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相關分類:
機率統計學 Probability-and-statistics、數學
海外代購書籍(需單獨結帳)
相關主題
商品描述
Optimal Statistical Decisions
This 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 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.
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
作者簡介
George E. P. Box, PhD, is Ronald Aylmer Fisher Professor Emeritus of Statistics and Industrial Engineering at the University of Wisconsin, Madison. His lifelong work has defined statistical analysis, while his name and research is a part of some of the most influential statistical constructs, including Box & Jenkins models, Box & Cox transformations, and Box & Behnken designs. Dr. Box is the coauthor of a number of Wiley books, including most recently, Statistical Control by Monitoring and Adjustment, Second Edition; Response Surfaces, Mixtures, and Ridge Analyses, Second Edition; and Improving Almost Anything: Ideas and Essays, Revised Edition.
Norman R. Draper is professor emeritus at the University of Wisconsin, Madison, in the Department of Statistics. His research interests include Experimental Design, Linear Models, and Nonlinear Estimation.
Howard Raiffa was an American academic who was the Frank P. Ramsey Professor of Managerial Economics, a joint chair held by the Business School and the Kennedy School of Government at Harvard University.
Robert Schlaifer is the author of Applied Statistical Decision Theory, published by Wiley.
