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Efficiency and Robustness of a Resampling M-Estimator in the Linear Model

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  • Hu, Feifang

Abstract

In the literature, there are basically two kinds of resampling methods for least squares estimation in linear models; the E-type (the efficient ones like the classical bootstrap), which is more efficient when error variables are homogeneous, and the R-type (the robust ones like the jackknife), which is more robust for heterogeneous errors. However, for M-estimation of a linear model, we find a counterexample showing that a usually E-type method is less efficient than an R-type method when error variables are homogeneous. In this paper, we give sufficient conditions under which the classification of the two types of the resampling methods is still true.

Suggested Citation

  • Hu, Feifang, 2001. "Efficiency and Robustness of a Resampling M-Estimator in the Linear Model," Journal of Multivariate Analysis, Elsevier, vol. 78(2), pages 252-271, August.
    • Handle: RePEc:eee:jmvana:v:78:y:2001:i:2:p:252-271
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