报告时间：2024年7月22日(周一)上午10：00

报告地点：数学与统计学院235报告厅

报告题目: Variable Selection Using the Method of the Broken Adaptive Ridge Regression

报告摘要: With data being more easily available than ever in the digital era, it is important that only relevant variables are retained when building a statistical model. In this talk, we consider variable selection methods incorporating the Broken Adaptive Ridge Regression (BAR) under a few different model frameworks that include joint modelling of recurrent and terminal events, the generalized partly linear models and partly linear Cox proportional hazards models. More specifically, we investigate methods for variable selection using BAR penalty in three projects. The BAR penalty can be summarized as an iteratively reweighted squared L2-penalized regression, which approximates the L0-regularization. Our method allows for the number of covariates to diverge with the sample size. Under certain regularity conditions, we prove that the BAR estimator is consistent and asymptotically normally distributed, which are known as the oracle properties in the variable selection literature. In our simulation studies, we compare our proposed method to the Minimum Information Criterion (MIC) method. We apply our method to the Medical Information Mart for Intensive Care (MIMIC-III) database, with the aim of investigating which variables affect the risks of repeated ICU admissions and death during ICU stay. In addition, we develop a new method for simultaneous variable selection and parameter estimation under the context of generalized partly linear models for data with high-dimensional covariates, we also implement the BAR penalty under the partly linear Cox proportional hazards model with right-censored data. We apply the method to the CATHGEN data with a binary response from a coronary artery disease study and obtain new findings in both high-dimensional genetic and low-dimensional non-genetic covariates. Finally, we apply our method to the acute respiratory disease syndrome (ARDS) to discover relevant metabolites that contribute to the risk of dying in the ICU.

报告人简介：卢学文，加拿大卡尔加里大学数学与统计系，终身教授，博士导师。主要从事数理统计和生物统计的研究工作。已发表SCI论文100多篇，在CRC和Springer出版高水平学术专著2部。2003年至今连续多次获得加拿大自然科学与工程研究理事会基金资助。主要社会兼职：加拿大统计协会（SSC）会员，美国数理统计学会(IMS)、泛华统计协会（ICSA）、加拿大食品安全研究所（CRIFS）等终身会员。美国Mathematical Reviews（《数学评论》）评论员，Journal of Statistical Computation and Simulation副主编，多个国际数学或概率统计期刊的编委。