Applied statistical decision theory应用统计决策论 夸克云 docx snb kindle pdf 网盘 azw3 下载

"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

Foreword

Preface and Introduction

Part I: Experimentation and Decision: General Theory

　1. The Problem and the Two Basic Modes of Analysis

　　1. Description of the Decision Problem

　　　1: The basic data; 2: Assessment of probability measures; 3: Example;

　　　4: The general decision problem as a game.

　　2. Analysis in Extensive Form

　　　1: Backwards induction; 2: Examplc.

　　3. Analysis in Normal Form

　　　1: Decision rules; 2: Performance, error, and utility characteristics; 3:Ex-ample; 4: Equivalence of the extensive and normal form; 5: Bayesian deci- sion theory as a completion of classical theory; 6: Informal choice of a decision rule.

　　4. Combination of Formal and Informal Analysis

　　　1: Unknown costs; cutting the decision tree; 2: Incomplete analysis of the decision tree; 3: Example.

　　5. Prior Weights and Consistent Behavior

　2. Sufficient Statistics and Noninformative Stopping

　　1. Introduction

　　　1: Simplifying assumptions; 2: Bayes' theorem; kernels

　　2. Sufficiency

　　　1: Bayesian definition of sufficiency; 2: Identification of sufficient statistics;

　　 3: Equivalence of the Bayesian and classical definitions of sufficiency; 4: Nuisance parameters and marginal sufficiency.

　　3. Noninformative Stopping

　　 1: Data-generating processes and stopping processes; 2: Likelihood of a sample; 3: Noninformative stopping processes; 4: Contrast between the Bayesian and classical treatments of stopping; 5: Summary.

　3. Conjugate Prior Distributions

　　1. Introduction; Assumptions and Definitions

　　 1: Desiderata for a family of prior distributions; 2: Sufficient statistics of fixed dimensionality.

2. Conjugate Prior Distributions

　　 1: Use of the sample kernel as a prior kernel; 2: The posterior distribution when the prior distribution is natural-conjugate; 3: Extension of the domain of the parameter; 4: Extension by introduction of a new parameter; 5: Con- spectus of natural-conjugate densities.

3. Choice and Interpretation of a Prior Distribution

　　 1: Distributions fitted to historical relative frequencies; 2: Distributions fitted to subjective betting odds; 3: Comparison of the weights of prior and sample evidence; 4: "Quantity of information" and "vague" opinions;

　　 5: Sensitivity analysis; 6: Scientific reporting.

4. Analysis in Extensive Form when the Prior Distribution and Sample Likelihood are Conjugate

　　 1: Definitions of terminal and preposterior analysis; 2: Terminal analysis;

　　 3: Preposterior analysis.

Part II: Extensive-Form Analysis When Sampling and Terminal Utilities Are Additive

　4. Additive Utility, Opportunity Loss, and the Value of Information:Introduction to Part II

　　1. Basic Assumptions

2. Applicability of Additive Utilities

　　3. Computation of Expected Utility

　　4. Opportunity Loss

　　 1: Definition of opportunity loss; 2: Extensive-form analysis using oppor-tunity loss instead of utility; 3: Opportunity loss when terminal and sam- piing utilities are additive; 4: Direct assessment of terminal opportunity losses; 5: Upper bounds on optimal sample size.

　　5. The Value of Information

　　　l: The value of perfect information; 2: The value of sample information and the net gain of sampling; 3: Summary of relations among utilities, op- portunity losses, and value of information.

　5A. Linear Terminal Analysis

　　1. Introduction

　　　1: The transformed state description co; 2: Terminal analysis.

　　2. Expected Value of Perfect Information when w is Scalar

　　　1: Two-action problems; 2: Finite-action problems; 3: Evaluation of linear- loss integrals; 4: Examples.

　　3. Preposterior Analysis

　　　1: The posterior mean as a random variable; 2: The expected value of sam- ple information.

　　4. The Prior Distribution of the Posterior Mean for Given e

　　　1: Mean and variance of ~"; 2: Limiting behavior of the distribution; 3　Limiting behavior of integrals when is scalar; 4: Exact distributions of;　5: Aouroximations to the distribution of : 6: Examt)les.

　　……

part III:Distribution Theory

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书籍介绍

This volume presents Raiffa and Schlaifer's groundbreaking examination of the applications of Bayesian statistical theory to real-life business problems of decision under conditions of uncertainty. The emphasis is on identifying a course of action that is logically consistent with the decision maker's own preference for consequences, as expressed by numerical utilities, and with the weight s/he attaches to unknown variables, as expressed by numerical probabilities. Well-organized with self-contained chapters, suitable for both complete and selected study of topics. Clear outline format and detailed table of contents allows for easy referencing. Extremely useful for its historical value. From the reviews: . "Raiffa and Schlaifer have contributed a major work and a major stimulus to further work. I hope especially that mathematical statisticians will read it and be encouraged to develop the fascinating lines of research that are suggested by it. I recommend it strongly to all statisticians seriously interested in applying statistics to real problems."--ASA Journal. "There is no comparable book. It should have an important effect on the future development of the subject." --Mathematical Reviews.