Statistical Decision Theory
Description
This monograph presents a radical rethinking of how elementary inferences should be made in statistics, implementing a comprehensive alternative to hypothesis testing in which the control of the probabilities of the errors is replaced by selecting the course of action (one of the available options) associated with the smallest expected loss.
Its strength is that the inferences are responsive to the elicited or declared consequences of the erroneous decisions, and so they can be closely tailored to the client's perspective, priorities, value judgments and other prior information, together with the uncertainty about them.
Reviews / Votes
From the book reviews:
"The definition of statistics assumed in the book is that of making decisions in the presence of uncertainty and with limited resources. . Although intended to communicate the relevance, feasibility and value of decision theory for 'everyday' (statistical) professionals, the book can also be of value as a reference in a graduate course in statistics." (Heleno Bolfarine, Mathematical Reviews, September, 2014)
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Content
Preface.- 1.Introduction.- 2.Estimating the Mean.- 3.Estimating the Variance.- 4.The Bayesian Paradigm.- 5.Data from other Distributions.- 6.Classification.- 7.Small-area Estimation.- 8.Study Design.- Index.