Minimum hellinger distance estimation for finite mixtures of Poisson regression models and its applications

被引：33

作者：

Lu, Z

Van Hui, Y

Lee, AH ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Syst Sci, Acad Math & Syst Sci, Beijing, Peoples R China

[2] City Univ Hong Kong, Dept Management Sci, Hong Kong, Hong Kong, Peoples R China

[3] Curtin Univ Technol, Dept Epidemiol & Biostat, Sch Publ Hlth, Perth, WA 6001, Australia

来源：

BIOMETRICS | 2003年 / 59卷 / 04期

关键词：

finite mixtures of Poisson regression models; maximum likelihood estimation; minimum Hellinger distance; outliers; robustness;

D O I：

10.1111/j.0006-341X.2003.00117.x

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Minimum Hellinger distance estimation (MHDE) has been shown to discount anomalous data points in a smooth manner with first-order efficiency for a correctly specified model. An estimation approach is proposed for finite mixtures of Poisson regression models based on MHDE. Evidence from Monte Carlo simulations suggests that MHDE is a viable alternative to the maximum likelihood estimator when the mixture components are not well separated or the model parameters are near zero. Biometrical applications also illustrate the practical usefulness of the MHDE method.

引用

页码：1016 / 1026

页数：11

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