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Trimmed, Bayesian and admissible estimators

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  • Jureckov√°, Jana
  • Klebanov, Lev B.
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    Abstract

    The authors proved in [5] 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 Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 42 (1999)
    Issue (Month): 1 (March)
    Pages: 47-51

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    Handle: RePEc:eee:stapro:v:42:y:1999:i:1:p:47-51

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    Related research

    Keywords: Admissibility Bayesian estimators Trimmed estimators;

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    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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