报告题目：Hidden Markov models with an unknown number of hidden states
时 间：2023年11月29日（星期三）16：15
地 点： 科研楼18号楼1233
主 办：数学与统计学院
参加对象：感兴趣的老师和学生
报告摘要：Hidden Markov models (HMMs) are valuable tools for analyzing longitudinal data due to their capability to describe dynamic heterogeneity. Conventional HMMs typically assume that the number of hidden states (i.e., the order of HMMs) is known or predetermined through criterion-based methods. This talk discusses double-penalized procedures for simultaneous order selection and parameter estimation for homogeneous and heterogeneous HMMs. We develop novel computing algorithms to address the challenges of updating the order. Furthermore, we establish the consistency of order and parameter estimators. Simulation studies show that the proposed procedures considerably outperform the commonly used criterion-based methods. An application to the Alzheimer's Disease Neuroimaging Initiative study further confirms the utility of the proposed method.
报告人简介：宋心远，香港中文大学统计学系正教授及系主任。主要研究方向为潜变量模型、贝叶斯方法、生存分析、非参数和半参数方法、统计计算等。担任或曾担任包括《Biometrics》《Electronic Journal of Statistics 》《Canadian Journal of Statistics》《Statistics and Its Interface》《Computational Statistics and Data Analysis》《Statistical Theory and Related Fields》《Psychometrika》 等多个国际期刊的副主编。