报告题目：Recent Advances in Hidden Markov Models: Inferences and Applications
报 告 人：宋心远 香港中文大学 教授
报告时间：2021年4月2日 下午 13:30-14:30
报告地点：腾讯会议
点击链接入会，或添加至会议列表：
https://meeting.tencent.com/s/pO4QmqI4Zn6x
会议 ID：540 245 947
校内联系人：赵世舜 zhaoss@jlu.edu.cn
报告摘要：
This talk presents recent advances in hidden Markov models (HMMs) for longitudinal data analysis. HMMs are commonly used to simultaneously investigate longitudinal observation process and the underlying dynamic transition process. The statistical inferences and applications of advanced HMMs are discussed. First, we develop a Bayesian adaptive group lasso procedure to conduct variable and function selection in the context of semiparametric HMMs. A basis expansion is used to approximate the nonparametric functions. Multivariate conditional Laplace priors are introduced to facilitate adaptive penalization on regression coefficients and various groups of basis expansions. An efficient Markov chain Monte Carlo algorithm is developed to identify important covariate and functional effects in the conditional and transition models. The proposed model is applied to the Alzheimer's Disease Neuroimaging Initiative study. Moreover, we discuss several recent developments on HMMs and possible extensions, such as the order selection of HMMs, varying-coefficient HMMs with zero-effect regions, quantile HMMs, and joint analysis of HMMs and survival models with longitudinal and time-to-event data.
报告人简介：
宋心远教授，香港中文大学统计系主任。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。
