Statistical Decision Theory

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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.

Author(s): Nicholas T. Longford (auth.)
Series: SpringerBriefs in statistics
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2013

Language: English
Pages: 124
City: Heidelberg
Tags: Statistics, general

Front Matter....Pages i-x
Introduction....Pages 1-15
Estimating the Mean....Pages 17-31
Estimating the Variance....Pages 33-47
The Bayesian Paradigm....Pages 49-64
Data from Other Distributions....Pages 65-77
Classification....Pages 79-93
Small-Area Estimation....Pages 95-109
Study Design....Pages 111-119
Back Matter....Pages 121-124