Introduction to Statistical Decision Theory

The Bayesian revolution in statistics - where statistics is integrated with decisionmaking in areas such as management, public policy, engineering, and clinical medicine - is here tostay. 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 makingunder uncertainty.Starting with an extensive account of the foundations of decision theory, theauthors develop the intertwining concepts of subjective probability and utility. They thensystematically and comprehensively examine the Bernoulli, Poisson, and Normal (univariate andmultivariate) data generating processes. For each process they consider how prior judgments aboutthe uncertain parameters of the process are modified given the results of statistical sampling, andthey investigate typical decision problems in which the main sources of uncertainty are thepopulation parameters. They also discuss the value of sampling information and optimal sample sizesgiven sampling costs and the economics of the terminal decision problems.Unlike most introductorytexts in statistics, Introduction to Statistical Decision Theory integrates statistical inferencewith decision making and discusses real-world actions involving economic payoffs and risks. Afterdeveloping the rationale and demonstrating the power and relevance of the subjective, decisionapproach, the text also examines and critiques the limitations of the objective, classicalapproach.