Semiparametric maximum likelihood estimation in Cox proportional hazards model with covariate measurement errors
學年 99
學期 1
出版(發表)日期 2010-09-01
作品名稱 Semiparametric maximum likelihood estimation in Cox proportional hazards model with covariate measurement errors
作品名稱(其他語言) 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
著者 Wen, Chi-Chung
單位 淡江大學數學學系
出版者 Heidelberg: Springer
著錄名稱、卷期、頁數 Metrika 72(2), pp.199-217
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.
關鍵字 Covariate measurement error;Cox model;Semiparametric maximum likelihood estimate;Profile likelihood
語言 en
ISSN 0026-1335; 1435-926X
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Wen, Chi-Chung
審稿制度
國別 DEU
公開徵稿
出版型式 紙本
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