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Minimum hellinger distance estimation for finite mixtures of Poisson regression models and its applications
Lu, Z; Van Hui, Y; Lee, AH
2003-12-01
Source PublicationBIOMETRICS
ISSN0006-341X
Volume59Issue:4Pages:1016-1026
AbstractMinimum 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.
Keywordfinite mixtures of Poisson regression models maximum likelihood estimation minimum Hellinger distance outliers robustness
Language英语
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiology ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000187501100031
PublisherBLACKWELL PUBLISHING LTD
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/18437
Collection中国科学院数学与系统科学研究院
Corresponding AuthorLee, AH
Affiliation1.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
Recommended Citation
GB/T 7714
Lu, Z,Van Hui, Y,Lee, AH. Minimum hellinger distance estimation for finite mixtures of Poisson regression models and its applications[J]. BIOMETRICS,2003,59(4):1016-1026.
APA Lu, Z,Van Hui, Y,&Lee, AH.(2003).Minimum hellinger distance estimation for finite mixtures of Poisson regression models and its applications.BIOMETRICS,59(4),1016-1026.
MLA Lu, Z,et al."Minimum hellinger distance estimation for finite mixtures of Poisson regression models and its applications".BIOMETRICS 59.4(2003):1016-1026.
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