Introduction to Statistical Decision Theory

Introduction to Statistical Decision Theory
John W. Pratt,Howard Raiffa,Robert Schlaifer | 1995-03-27 00:00:00 | The MIT Press | 895 | Economics
The Bayesian revolution in statistics - where statistics is integrated with decision making in areas such as management, public policy, engineering, and clinical medicine - is here to stay. Introduction to Statistical Decision Theory states the case and in a self-contained, comprehensive way shows how the approach is operational and relevant for real-world decision making under uncertainty. Starting with an extensive account of the foundations of decision theory, the authors develop the intertwining concepts of subjective probability and utility. They then systematically and comprehensively examine the Bernoulli, Poisson, and Normal (univariate and multivariate) data generating processes. For each process they consider how prior judgments about the uncertain parameters of the process are modified given the results of statistical sampling, and they investigate typical decision problems in which the main sources of uncertainty are the population parameters. They also discuss the value of sampling information and optimal sample sizes given sampling costs and the economics of the terminal decision problems. Unlike most introductory texts in statistics, Introduction to Statistical Decision Theory integrates statistical inference with decision making and discusses real-world actions involving economic payoffs and risks. After developing the rationale and demonstrating the power and relevance of the subjective, decision approach, the text also examines and critiques the limitations of the objective, classical approach.
Reviews
This is a very accessible introduction to Bayesian statistics and its applications in economics and management science. The book would make an interesting and useful two semester course at the sophomore or junior level. The mathematical prerequisites are small - some calculus, but the book manages to introduce basic probability, stochastic processes, statistical inference, large sample theory, the multivariate normal distribution, and topics related to economics such as utility functions, lotteries, decision trees and portfolio theory. These topics are useful to students in a wide variety of disciplines: economics, finance, computer science, electrical engineering, statistics, and students in the sciences which may end up in the bio/health sciences (medial decision making). In addition with the growing prevalence of Bayesian computation across disciplines, it seems like a lost opportunity not to offer such a course.

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