报告题目：Composite quantile regression analysis of survival data with missing cause-of-failure information and its application to breast cancer clinical trial

报告人：邹玉叶

单位：上海海事大学经济管理学院

时间：2023年3月22日11:00

腾讯会议：241-971-878

线下会场：学院二楼会议室

摘要：In this talk, wepropose weighted composite quantile regression (CQR) for estimating a lot of quantile regression (QR) of survival data based on single-index coefficient model (SICM), which is a very general and flexible tool for exploring the relationship between response variable and a set of predictors. The statistical inference for SICM is considered when cause-of-failure information (censored or non-censored) is always observed. However, the cause-of-failure information may be missing at random (MAR) for various reasons. Regression calibration, imputation and inverse probability weighted approaches are applied to deal with the MAR assumption. The asymptotic normality of the proposed estimators are established. Meanwhile, the oracle property of the variable selection based on adaptive LASSO penalty procedure is conducted. To assess the finite sample performance of the proposed estimators, simulation study with normal error and heavy-tail error are considered. As expected, the CQR estimators perform as good as the least-square estimators for normal error, and are more robust to heavy-tailed error. Finally, a breast cancer real data analysis is carried out to illustrate the proposed methodologies.

简介：邹玉叶，博士，上海海事大学经济管理学院副教授，主持一项国家级项目，一项省部级重点项目和一项省部级一般项目。在《Computational Statistics and Data Analysis》、《Journal of Statistical Planning and Inference》、《Statistical Papers》、《Journal of Systems Science and Complexity》等国内外重要学术刊物上发表学术论文近二十篇。