An asymptotic theory for the nonparametric maximum likelihood estimator in the Cox gene model

University of Taipei > 理學院 > 數學系(含數學教育碩士班) > 期刊論文 > Item 987654321/2689

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题名:	An asymptotic theory for the nonparametric maximum likelihood estimator in the Cox gene model
作者:	I-Shou Chang Chao Agnes Hsiung Mei-Chuan Wang Chi-Chung Wen 王美娟溫啓仲
贡献者:	臺北市立教育大學數學資訊教育學系
关键词:	age at onset asymptotic normality Cox gene model discrete frailty model identifiability nonparametric maximum likelihood estimate profile likelihood information
日期:	2005
上传时间:	2009-08-04 09:57:16 (UTC+8)
摘要:	The Cox model with a gene effect for age at onset was introduced and studied by Li, Thompson and Wijsman. We study the nonparametric maximum likelihood estimation of the gene effect and the regression coefficient in this model. We indicate conditions under which the parameters are identifiable and the nonparametric maximum likelihood estimate is consistent and asymptotically normal. We also apply the theory of observed profile information to obtain a consistent estimate of the asymptotic variance. Besides providing theoretical support for Li et al., our work provides an alternative approach to the numerical methods in this model.
關聯:	Bernoulli, V11(5) , p.863-892.
显示于类别:	[數學系(含數學教育碩士班)] 期刊論文

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