数学与统计学院学术报告[2020] 092号

(高水平大学建设系列报告445号)

报告题目:  Joint Analysis of Mixed Types of Outcomes with Latent Variables

报告人：潘灯（华中科技大学）

报告时间：2020年11月13日周五下午14:00-15:00　　　　　　　　　　　　 　　

报告地点：腾讯会议 会议号码：578840303　　　　 　 　　　　　　　　　　　　　

报告内容：We propose a joint modeling approach to investigating the observed and latent risk factors of mixed types of outcomes. The proposed model comprises three parts. The first part is an exploratory factor analysis model that summarizes latent factors through multiple observed variables. The second part is a proportional hazards model that examines the observed and latent risk factors of multivariate time-to-event outcomes. The third part is a linear regression model that investigates the determinants of a continuous outcome. We develop a Bayesian approach coupled with MCMC methods to determine the number of latent factors, the association between latent and observed variables, and the important risk factors of different types of outcomes. A modified stochastic search item selection algorithm, which introduces normal-mixture-inverse gamma priors to factor loadings and regression coefficients, is developed for simultaneous model selection and parameter estimation. The proposed method is subjected to simulation studies for empirical performance assessment and then applied to a study concerning the risk factors of type 2 diabetes and the associated complications.

报告人简历:  潘灯，女，华中科技大学数学与统计学院副教授。2014年于香港中文大学获博士学位。主要研究方向是贝叶斯统计，结构方程模型，生存分析等，在统计学权威期刊Journal of the American Statistical Association, Statistical Methods in Medical research等发表论文数篇。曾主持国家自然科学基金（青年项目）一项。

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数学与统计学院

2020年11月11日