报告人：Song Xinyuan (宋心远教授，香港中文大学)

报告题目：Bayesian Semiparametric Mixed Hidden Markov Model

报告摘要：Alzheimer's disease (AD) is a firmly incurable and progressive disease. The pathology of AD usually evolves from cognitive normal (CN), to mild cognitive impairment (MCI), to AD. The aim of this study is to develop a Bayesian hidden Markov model (HMM) to characterize disease pathology, identify hidden states corresponding to the diagnosed stages of cognitive decline, and examine the dynamic changes of potential risk factors associated with the CN-MCI-AD transition. The HMM framework consists of two major components. The first one is a state-dependent semiparametric regression for delineating the complex associations between clinical outcomes of interest and a set of prognostic biomarkers across neurodegenerative states. The second one is a parametric transition model, while accounting for potential covariate effects on the cross-state transition. The inter-individual and inter-process differences are taken into account via correlated random effects in both components. Based on the Alzheimer's Disease Neuroimaging Initiative dataset, we are able to identify four states of AD pathology, corresponding to common diagnosed cognitive decline stages, including CN, early MCI, late MCI, and AD and examine the effects of hippocampus, age, gender, and APOE gene on degeneration of cognitive function across the four cognitive states.

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： 2018年7月12日（星期四）上午10：30 - 11：30

报告地点：科技楼南楼602学术报告厅