报告题目：Bayesian transformation models with partly interval-censored data
报 告 人：王纯杰 教授
所在单位：长春工业大学
报告时间：2022年9月5日 14：00-15：00
报告地点：腾讯会议 ID：533-459-182 会议密码：0905
校内联系人：王培洁 wangpeijie@jlu.edu.cn
报告摘要：In many scientific fields, partly interval-censored data, which consist of exactly observed and interval-censored observations on the failure time of interest, appear frequently. However, methodological developments in the analysis of partly interval-censored data are relatively limited and have mainly focused on additive or proportional hazards models. The general linear transformation model provides a highly flexible modeling framework that includes several familiar survival models as special cases. Despite such nice features, the inference procedure for this class of models has not been developed for partly interval-censored data. We propose a fully Bayesian approach coped with efficient Markov chain Monte Carlo methods to fill this gap. A four-stage data augmentation procedure is introduced to tackle the challenges presented by the complex model and data structure. The proposed method is easy to implement and computationally attractive. The empirical performance of the proposed method is evaluated through two simulation studies, and the model is then applied to a dental health study.
报告人简介：王纯杰，教授，博士生导师，现任数学与统计学院经理兼党委副书记，吉林省拔尖创新人才，吉林省高水平优势特色学科统计学首席负责人，统计学国家一流专业负责人，省级黄大年式统计学教学科研团队负责人。曾先后赴美国密苏里大学统计系学习、香港中文大学统计系访问，新加坡南洋理大学统计系做博士后研究。中国概率统计研究会理事，中国现场统计研究会理事，全国工业统计学教学研究会常务理事，中国商业统计学会常务理事，中国现场统计研究会大数据统计分会常务理事，吉林省现场统计研究会副秘书长等。主持国家自然科学基金面上项目1项，青年基金项目1项，主要参与国家自然科学基金面上项目2项，主持省汽车重大专项1项等，发表科研论文69篇，其中SCI论文19篇等。
