Trimmed, Bayesian and admissible estimators
AbstractThe authors proved in  that the robust M- and L-estimators of location, which are independent of the extreme order statistics of the sample, cannot be admissible with respect to L1 risk in the class of translation equivariant estimators. This result is now extended in two respects: (i) We show that these estimators cannot be even Bayesian, under some regularity conditions, with respect to a strictly convex and continuously differentiable loss function; (ii) moreover, we extend the result to the linear regression model and show the inadmissibility of regression equivariant estimators, trimming-off the observations with nonpositive [nonnegative] residuals with respect to [alpha]1- [[alpha]2]-regression quantiles, respectively, for some 0
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 42 (1999)
Issue (Month): 1 (March)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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