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    題名: Semiparametric maximum likelihood estimation in Cox proportional hazards model with covariate measurement errors
    其他題名: 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
    貢獻者: 淡江大學數學學系
    關鍵詞: Covariate measurement error;Cox model;Semiparametric maximum likelihood estimate;Profile likelihood
    日期: 2010-09
    上傳時間: 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
    顯示於類別:[數學學系暨研究所] 期刊論文

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