报告题目：Quantile regression for partially linear varying-coefficient model with censoring indicators missing at random

报 告 人：梁汉营 教授（同济大学）

报告时间：2019年6月10日，16:00-17:00

报告地点：知新楼B-1238

报告摘要：

In this talk, we focus on the partially linear varying-coefficient quantile regression model when the data are right censored and the censoring indicator is missing at random. Based on the calibration and imputation methods, a three-stage approach is proposed to construct the estimators of the linear part and the nonparametric varying-coefficient function for this model. At the same time, we discuss the variable selection of the covariates in the linear part by adopting adaptive LASSO penalty. Under appropriate assumptions, the asymptotic normality of the proposed estimators is established, and the penalized estimators are proven to have the oracle property. Simulation study and a real data analysis are conducted to evaluate the performance of the proposed estimators.

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