Robust estimates in generalized partially linear models
Methodology
2011-11-10 v1
Abstract
In this paper, we introduce a family of robust estimates for the parametric and nonparametric components under a generalized partially linear model, where the data are modeled by with \mu_i=H(\eta(t_i)+\mathbf{x}_i^{\mathrm{T}}\beta), for some known distribution function F and link function H. It is shown that the estimates of are root-n consistent and asymptotically normal. Through a Monte Carlo study, the performance of these estimators is compared with that of the classical ones.
Cite
@article{arxiv.0708.0165,
title = {Robust estimates in generalized partially linear models},
author = {Graciela Boente and Xuming He and Jianhui Zhou},
journal= {arXiv preprint arXiv:0708.0165},
year = {2011}
}
Comments
Published at http://dx.doi.org/10.1214/009053606000000858 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)