【下载】Statistical Decision Theory and Bayesian Analysis~James O. Berger.1985

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Statistical Decision Theory and Bayesian Analysis (Springer Series in Statistics) [Hardcover]
James O. Berger (Author)

Editorial Reviews

Review

"The outstanding strengths of the book are its topic coverage, references, exposition, examples and problem sets... This book is an excellent addition to any mathematical statistician's library."
(Bulletin of the Am. Mathematical Soc.)

Product Description

In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes, the book can be used as a self-contained introduction to Bayesian analysis. In addition, much of the decision-theoretic portion of the text has been updated, including new sections covering such modern topics as minimax multivariate (Stein) estimation.

Product Details Hardcover: 624 pages Publisher: Springer; 2nd edition (August 21, 1985) Language: English ISBN-10: 0387960988 ISBN-13: 978-0387960982

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关键词：Statistical statistica statistic Analysis Decision Analysis Theory Bayesian Decision James

CHAPTER 1
Basic Concepts
1.1 Introduction
1.2 Basic Elements
1.3 Expected Loss, Decision Rules, and Risk
1.3.1 Bayesian Expected Loss
1.3.2 Frequentist Risk
1.4 Randomized Decision Rules
1.5 Decision Principles
1.6 Foundations
1. 7 Sufficient Statistics
1.8 Convexity
Exercises
  
CHAPTER 2
Utility and Loss
2.1 Introduction
2.2 Utility Theory
2.3 The Utility of Money
2.4 The Loss Function
2.5 Criticisms
Exercises

CHAPTER 3
Prior Information and Subjective probability
3.1 Subjective Probability
3.2 Subjective Determination of the Pnor Density
3.3 Noninformative Priors
3.4 Maximum Entropy Priors .'
3.5 Usin g the Marginal Distribution to Determme the Pnor 3.5
3.6 Hierarchical Priors
3.7 Criticisms
3.8 The Statistician's Role
Exercises

CHAPTER 4
Bayesian Analysis
4.1 Introduction
4.2 The Posterior Distribution
4.3 Bayesian Inference
4.4 Bayesian Decision Theory .
4.5 Empirical Bayes Analysis
4.6 Hierarchical Bayes Analysis
4.6.1 Introduction
4.7 Bayesian Robustness
4.8 Admissibility of Bayes Rules and Long Run Evaluations
4.9 Bayesian Calculation
4.10 Bayesian Communication
4.11 Combining Evidence and Group Decisions
4.12 Criticisms
Exercises

CHAPTER 5
Minimax Analysis
5.1 Introduction
5.2 Game Theory
5.3 Statistical Games
5.4 Classes of Minimax Estimators
5.5 Evaluation of the Minimax Principle
Exercises

CHAPTER 6
Invariance
6.1 Introduction
6.2 Formulation
6.3 Location Parameter Problems
6.4 Other Examples of Invariance
6.5 Maximal Invariants
6.6 Invariance and Noninformative Priors . .
6.7 Invariance and Minimaxity
6.8 Admissibility of Invariant Rules
6.9 Conclusions
Exercises

CHAPTER 7
Preposterior and Sequential Analysis
7.1 Introduction
7.2 Optimal Fixed Sample Size
7.3 Sequential Analysis-Notation
7.4 Bayesian Sequential Analysis
7.5 The Sequential Probability Ratio Test
7.6 Minimax Sequential Procedures
7.7 The Evidential Relevance of the Stopping Rule
7.8 Discussion of Sequential Loss Functions
Exercises

CHAPTER 8
Complete and Essentially Complete Classes
8.1 Preliminaries
8.2 Complete and Essentially Complete Classes from Earlier Chapters
8.3 One-Sided Testing
8.4 Monotone Decision Problems
8.5 Limits of Bayes Rules
8.6 Other Complete and Essentially Complete Classes of Tests
8.7 Complete and Essentially Complete Classes in Estimation
8.8 Continuous Risk Functions
8.9 Proving Admissibility and Inadmissibility

Exercises

APPENDIX 1
Common Statistical Densities
I Continuous
II Discrete

APPENDIX 2
Supplement to Chapter 4
I Definition and Properties of Hm
II Development of (4.121) and (4.122)
III Verification of Formula (4.123)

APPENDIX 3
Technical Arguments from Chapter 7
I Verification of Formula (7.8)
II Verification of Formula (7.10)

Bibliography
Notation and Abbreviations
Author Index
Subject Index

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很重要的教材書

1# kxjs2007
我终于下到了 费了好大的力气！！！！！！！！！

多谢楼主给力的分享～

BFS初学者，急需学习材料啊 2# kxjs2007

好书。。终于找到了。。

謝謝樓主的分享

非常感谢。。