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

Feifang Hu

Journal of Multivariate Analysis, 2001, vol. 78, issue 2, 252-271

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.

Keywords: bootstrap; jackknife; M-estimator; resampling; method; variance; estimations; E-type; R-type (search for similar items in EconPapers)
Date: 2001
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