Joint conditional likelihood estimator in logistic regression with missing covariate data
學年 90
學期 2
出版（發表）日期 2002-04-01
作品名稱 Joint conditional likelihood estimator in logistic regression with missing covariate data
作品名稱（其他語言）
著者 Wang, C. Y.; Chen, J. C.; Lee, S. M.; Ou, S. T.
單位 淡江大學統計學系
出版者 Taipei: Academia Sinica * Institute of Statistical Science
著錄名稱、卷期、頁數 Statistica Sinica 12(2), pp.555-574
摘要 This article considers semiparametric estimation in logistic regression with missing covariates. In a validation subsample, we assume covariates are measured without error. Some covariates are missing in the non-validation set, while surrogate variables may be available for all study subjects. We consider the case when a covariate variable is missing at random such that the selection probability of the validation set depends only on observed data. Breslow and Cain (1988) proposed a conditional likelihood approach based on the validation set. We combine the conditional likelihoods of the validation set and the non-validation set. The proposed estimator is easy to implement and is semiparametric since no additional model assumption is imposed. Large sample theory is developed. For the estimation of the parameter for the missing covariate, simulations show that, under various situations, the proposed estimator is significantly more efficient than the validation likelihood estimator of Breslow and Cain and the inverse selection probability weighted estimator. Under moderate sample sizes and moderate values of relative risk parameters, our estimator remains competitive when compared with the nonparametric maximum likelihood estimator of Scott and Wild (1997). The proposed method is illustrated by a real data example.
關鍵字 Estimators;Maximum likelihood estimation;Estimation bias;Consistent estimators;Statistical estimation;Logistic regression;Estimators for the mean;Cigarette smoking;Analytical estimating;Correlation coefficients
語言 en_US
ISSN 1017-0405
期刊性質 國內
收錄於
產學合作
通訊作者
審稿制度 否
國別 TWN
公開徵稿
出版型式 ,紙本
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