Semiparametric maximum likelihood estimation in Cox proportional hazards model with covariate measurement errors

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题名:	Semiparametric maximum likelihood estimation in Cox proportional hazards model with covariate measurement errors
其它题名:	具共變量測量誤差之柯斯正比風險模型半母數最大概似估計
作者:	Wen, Chi-Chung
贡献者:	淡江大學數學學系
关键词:	Covariate measurement error;Cox model;Semiparametric maximum likelihood estimate;Profile likelihood
日期:	2010-09
上传时间:	2011-05-20 09:42:36 (UTC+8)
出版者:	Heidelberg: Springer
摘要:	This paper studies semiparametric maximum likelihood estimators in the Cox proportional hazards model with covariate error, assuming that the conditional distribution of the true covariate given the surrogate is known. We show that the estimator of the regression coefficient is asymptotically normal and efficient, its covariance matrix can be estimated consistently by differentiation of the profile likelihood, and the likelihood ratio test is asymptotically chi-squared. We also provide efficient algorithms for the computations of the semiparametric maximum likelihood estimate and the profile likelihood. The performance of this method is successfully demonstrated in simulation studies.
關聯:	Metrika 72(2), pp.199-217
DOI:	10.1007/s00184-009-0248-1
显示于类别:	[數學學系暨研究所] 期刊論文

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