数学与统计学院学术报告[2020] 113号

(高水平大学建设系列报告466号)

报告题目:  Bayesian Analysis of Semiparametric Hidden Markov Models with Latent Variables

报告人：蔡敬衡（中山大学）

报告时间：2020年11月20日周五上午11:00-12:00　　　　　　　　　　　　 　　

报告地点：腾讯会议 会议号码：781692274　 　　　　　　　　　　　　

报告内容：In psychological, social, behavioral, and medical studies, hidden Markov models (HMMs) have been extensively applied to the simultaneous modeling of heterogeneous observation and hidden transition in the analysis of longitudinal data. However, the majority of the existing HMMs are developed in a parametric framework without latent variables. This study considers a novel semiparametric HMM, which comprises a semiparametric latent variable model to investigate the complex interrelationships among latent variables and a nonparametric transition model to examine the linear and nonlinear effects of potential predictors on hidden transition. The Bayesian P-splines approach and Markov chain Monte Carlo methods are developed to estimate the unknown functions and parameters. Penalized expected deviance, a Bayesian model comparison statistic, is employed to conduct model comparison. The empirical performance of the proposed methodology is evaluated through simulation studies. An application to a data set derived from the National Longitudinal Survey of Youth is presented.

报告人简历:  蔡敬衡，中山大学数学学院统计系副教授。主要研究贝叶斯分析、含潜变量的复杂模型建模与分析，在Psychometrika、Bayesian Analysis、Statistical Methods in Medical Research等杂志发表十多篇论文。

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数学与统计学院

2020年11月20日

（本文最近更新于2020/11/20 8:59:00　累计点击数:89）