Statistical Decision Theory /
Longford, Nicholas T.
Statistical Decision Theory / by Nicholas T. Longford. - x, 124 páginas 23 ilustraciones recurso en línea. - SpringerBriefs in Statistics, 2191-544X .
Springer eBooks
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.
9783642404337
10.1007/9783642404337 doi
QA276-280
Statistical Decision Theory / by Nicholas T. Longford. - x, 124 páginas 23 ilustraciones recurso en línea. - SpringerBriefs in Statistics, 2191-544X .
Springer eBooks
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.
9783642404337
10.1007/9783642404337 doi
QA276-280