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Statistical Decision Theory [electronic resource] / by Nicholas T. Longford.

By: Longford, Nicholas T [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Statistics: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: X, 124 p. 23 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642404337.Subject(s): Statistics | Statistics | Statistics, generalDDC classification: 519.5 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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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.

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.

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