On least favourable two point priors and minimax estimators under absolute error loss

On least favourable two point priors and minimax estimators under absolute error loss

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Article ID: iaor19942022
Country: Germany
Volume: 40
Start Page Number: 283
End Page Number: 298
Publication Date: Jan 1993
Journal: Metrika
Authors: ,
Abstract:

In case of absolute error loss the authors investigate for an arbitrary class of probability distributions, if or if not a two point prior can be least favourable and a corresponding Bayes estimator can be minimax when the parameter is restricted to a closed and bounded interval of ℝ. The general results are applied to several examples, for instance location and scale parameter families are considered. The authors give examples for which, independent of the length of the parameter interval, no two point priors exist. On the other hand examples are given having a least favourable two point prior when the parameter interval is sufficiently small.

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