Please use this identifier to cite or link to this item:
Title: Efficiency and robustness of a resampling M-estimator in the linear model
Authors: Hu, F. 
Keywords: Bootstrap
resampling method
variance estimations
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.
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
ISSN: 0047259X
DOI: 10.1006/jmva.2000.1951
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.