报告人：Song Xinyuan (宋心远教授，香港中文大学)

报告题目：Statistical Analysis of Semiparametric Hidden Markov Models

报告摘要：In this study, we develop a semiparametric hidden Markov model to analyze longitudinal data. The proposed model comprises a parametric transition model for examining how potential predictors influence the probability of transition from one state to another and a nonparametric conditional model for revealing the functional effects of explanatory variables on responses of interest. Unlike conventional regression that focuses only on the observation process, the proposed model simultaneously investigates the observation process and the underlying transition process. Two correlated random effects, the one is in the conditional model and the other is in the transition model, are considered to describe the possible dependency within and/or between the two stochastic processes. We propose a Bayesian approach that combines Bayesian P-splines and MCMC methods to conduct the statistical analysis. The empirical performance of the proposed methodology is evaluated via simulation studies. An application to a study on Alzheimer’s disease is presented.

报告人简介：宋心远，香港中文大学统计系教授，香港中文大学理学院助理院长。宋心远教授的研究方向是潜变量模型，贝叶斯方法，统计计算和生存分析等。同时还担任多个国际期刊包括《Psychometrika》，《Biometrics》，《Computational Statistics & Data Analysis》和《Structural Equation Modeling: A Multidisciplinary Journal》的副主编或编委。已在国际期刊发表超过100篇论文，近期论文主要发表于《Journal of the American Statistical Association》，《Biometrika》，《Biometrics》，《Bioinformatics》，《Psychometrika》，《Quantitative Finance》等期刊。

报告时间：2017年12月15日（星期五）下午3：30 - 4：30

报告地点：科技楼南楼702