The Annals of Statistics

Asymptotic Lower Bounds for Risk in Robust Estimation

Rudolf Beran

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Abstract

Robustness and efficiency of a parameter estimate $T$ can be assessed by comparing the fitted parametric distribution $P_T$ with the actual distribution, which is assumed to lie near the parametric family $\{P_\theta:\theta\in\Theta\}$. Asymptotic lower bounds are established for the minimax risk over distributions near the parametric model, taking as loss function a monotone increasing function of the Hellinger distance between the actual distribution of the sample and the fitted distribution determined by $T$. The set of marginal distributions considered in the minimax calculation is a subset of the Hellinger ball of radius $O(n^{-1/2})$ centered at $P_\theta, n$ being the sample size. When the loss function is bounded, the lower bound on maximum risk can be attained asymptotically. However, an estimator of $\theta$ which is asymptotically minimax for bounded loss functions may be far from optimal when the loss function is unbounded. Such divergent behavior is exhibited, for instance, by the sample mean in nearly normal models.

Article information

Source
Ann. Statist., Volume 8, Number 6 (1980), 1252-1264.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176345198

Digital Object Identifier
doi:10.1214/aos/1176345198

Mathematical Reviews number (MathSciNet)
MR594642

Zentralblatt MATH identifier
0453.62032

JSTOR
links.jstor.org

Subjects
Primary: 62G35: Robustness
Secondary: 62F10: Point estimation

Keywords
Robust estimation parametric models risk asymptotic minimax bounds asymptotic minimax estimators

Citation

Beran, Rudolf. Asymptotic Lower Bounds for Risk in Robust Estimation. Ann. Statist. 8 (1980), no. 6, 1252--1264. doi:10.1214/aos/1176345198. https://projecteuclid.org/euclid.aos/1176345198


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