Robust estimation in generalized linear models: the density power divergence approach

被引：0
作者：
Abhik Ghosh
Ayanendranath Basu
机构：
[1] Indian Statistical Institute,Interdisciplinary Statistical Research Unit
来源：
TEST | 2016年 / 25卷
关键词：
Density power divergence; Generalized linear model ; Logistic regression; Poisson regression; Robustness; 62F35; 62J12;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The generalized linear model is a very important tool for analyzing real data in several application domains where the relationship between the response and explanatory variables may not be linear or the distributions may not be normal in all the cases. Quite often such real data contain a significant number of outliers in relation to the standard parametric model used in the analysis; in such cases inference based on the maximum likelihood estimator could be unreliable. In this paper, we develop a robust estimation procedure for the generalized linear models that can generate robust estimators with little loss in efficiency. We will also explore two particular special cases in detail—Poisson regression for count data and logistic regression for binary data. We will also illustrate the performance of the proposed estimators through some real-life examples.
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页码：269 / 290
页数：21
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