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

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Title:	An asymptotic theory for the nonparametric maximum likelihood estimator in the Cox gene model
Authors:	I-Shou Chang Chao Agnes Hsiung Mei-Chuan Wang Chi-Chung Wen 王美娟溫啓仲
Contributors:	臺北市立教育大學數學資訊教育學系
Keywords:	age at onset asymptotic normality Cox gene model discrete frailty model identifiability nonparametric maximum likelihood estimate profile likelihood information
Date:	2005
Issue Date:	2009-08-04 09:57:16 (UTC+8)
Abstract:	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.
Relation:	Bernoulli, V11(5) , p.863-892.
Appears in Collections:	[Department of Mathematic and Computer Science Education] Periodical Articles

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