Inference on Semiparametric Transformation Model with General Interval-censored Failure Time Data
报告人：王纯杰
报告地点：数学与统计楼415室
报告时间：2019年01月17日星期四15:45-16:30
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报告摘要：
Variable selection is a crucial issue in model building and it has received considerable attention in the literature of survival analysis. However, available approaches in this direction have mainly focused on time-to-event data with right censoring. Moreover, a majority of existing variable selection procedures for survival models are developed in a frequentist framework. In this article, we consider additive hazards models in the presence of current status data. We propose a Bayesian adaptive least absolute shrinkage and selection operator (lasso) procedure to conduct a simultaneous variable selection and parameter estimation. Efficient Markov chain Monte Carlo (MCMC) methods are developed to implement posterior sampling and inference. The empirical performance of the proposed method is demonstrated by simulation studies. An application to a study on the risk factors of heart failure disease for type 2 diabetes patients is presented.
主讲人简介：
王纯杰，长春工业大学数学与统计学院，教授，博士生导师，吉林省第六批拔尖创新人才第三层次人才称号，数学与统计学院副院长（主持工作）。中国现场统计研究会经济与金融统计分会常务理事，中国现场统计研究会大数据统计分会常务理事，环境与资源统计分会理事，全国工业统计学教学研究会理事，吉林省现场统计研究会副秘书长；吉林省工业与数学研究会常务理事。近年来先后访问美国密苏里大学统计系，香港中文大学统计系（长白山学者讲座教授）和新加坡南洋理工大学数学系（向黎明教授），主持国家自然科学基金青年基金1项，面上项目1项，主持和参与其他省部级项目13项。发表SCI论文10篇。
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