Sieve Maximum Likelihood Regression Analysis of Dependent Current Status Data
主 题: Sieve Maximum Likelihood Regression Analysis of Dependent Current Status Data
报告人: 胡涛 (首都师范大学数学科学学院)
时 间: 2015-05-28 14:00 - 15:00
地 点: 理科一号楼 1114
Current status data occur in contexts including demographic studies and tumorigenicity experiments. In this case, each subject is observed only once and the failure time of interest is either left- or right-censored. Many methods have been developed for the analysis of such data , most of which assume that the failure time and the observation time are independent completely or given covariates, which may not hold in many situations. In this paper, we present a sieve maximum likelihood approach for current status data when independence does not hold. A copula model and monotone I-splines are used and the asymptotic properties of the resulting estimators are established. In particular, the estimated regression parameters are shown to be semiparametrically efficient. An illustrative example is provided
