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|Title:||Efficiency and robustness of a resampling M-estimator in the linear model||Authors:||Hu, F.||Keywords:||Bootstrap
|Issue Date:||Aug-2001||Citation:||Hu, F. (2001-08). Efficiency and robustness of a resampling M-estimator in the linear model. Journal of Multivariate Analysis 78 (2) : 252-271. ScholarBank@NUS Repository. https://doi.org/10.1006/jmva.2000.1951||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. © 2001 Academic Press.||Source Title:||Journal of Multivariate Analysis||URI:||http://scholarbank.nus.edu.sg/handle/10635/105107||ISSN:||0047259X||DOI:||10.1006/jmva.2000.1951|
|Appears in Collections:||Staff Publications|
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