报告题目：Functional Concurrent Hidden Markov Model

报告时间：2023年10月18日下午3:00-

报告地点：格物楼5楼数学研究中心报告厅

报告摘要：This study considers a functional concurrent hidden Markov model. The proposed model consists of two components. One is a transition model for elucidating how potential covariates influence the transition probability from one state to another. The other is a conditional functional linear concurrent regression model for characterizing the state-specific effects of functional covariates. A distribution-free random effect is introduced to the conditional model to describe the dependency of individual functional observations. The soft-thresholding operator and the adaptive group lasso are introduced to simultaneously accommodate the local and global sparsity of the functional coefficients. A Bayesian approach is developed to jointly conduct estimation, variable selection, and the detection of zero-effect regions. This proposed approach incorporates the dependent Dirichlet process with stick-breaking prior for accommodating the unspecified distribution of the random effect and a blocked Gibbs sampler for efficient posterior sampling. Finally, the empirical performance of the proposed method is evaluated through simulation studies, and the utility of the methodology is demonstrated by an application to the analysis of air pollution and meteorological data.

报告人简介：宋心远，香港中文大学统计学系正教授、系主任，教育部长江学者讲座教授。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。在统计学和心理测量学领域担任多个国际期刊的副主编，包括Biometrics、Electronic Journal of Statistics、Canadian Journal of Statistics、Statistics and Its Interface、Computational Statistics and Data Analysis、Psychometrika和Structural Equation Modeling: A Multidisciplinary Journal等。