学术报告题目: Hidden Markov models with an unknown number of hidden states

报告人：宋心远教授(香港中文大学)

报告时间：2023年10月19日(周四)上午10:00-12:00

报告地点：数理楼235报告厅

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.

报告人简历:

宋心远，香港中文大学统计学系教授、系主任，长江学者讲座教授。她的研究兴趣主要包括潜变量模型、生存分析模型和贝叶斯模型，非参数和半参数方法及统计计算。宋教授正担任（或曾担任）多个统计学国际顶级和知名期刊的副主编，包括但不限于Biometrics, Electronic Journal of Statistics, Canadian Journal of Statistics, Statistics and Its Interface, Computational Statistics and Data Analysis, Psychometrika, and Structural Equation Modeling: A Multidisciplinary Journal.