Robust estimators for generalized linear models

被引：32
|
作者：
Valdora, Marina [1 ]
Yohai, Victor J. [1 ,2 ]
机构：
[1] Univ Buenos Aires, RA-1053 Buenos Aires, DF, Argentina
[2] Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2014年 / 146卷
关键词：
M-estimators; Transformations; Breakdown point; LOGISTIC-REGRESSION MODEL; OUTLIERS;
D O I：
10.1016/j.jspi.2013.09.016
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
In this paper we propose a family of robust estimators for generalized linear models. The basic idea is to use an M-estimator after applying a variance stabilizing transformation to the response. We show the consistency and asymptotic normality of these estimators. We also obtain a lower bound for their breakdown point. A Monte Carlo study shows that the proposed estimators compare favorably with respect to other robust estimators for generalized linear models with Poisson response and log link. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：31 / 48
页数：18
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