时间：12月24日周四下午1:30-2:30
地点：统计楼103会议室
报告人：张伟平教授 中国科学技术大学统计与金融系系主任。张伟平教授的研究方向是贝叶斯理论，混合效应模型，生物信息论以及生物统计等，在Biometrika, JASA, JRSS.B等知名统计学刊物上发表论文30余篇。
题目：Mean-Correlation Regression for Discrete Longitudinal Responses
摘要：Joint mean-covariance regression modelling with unconstrained parameterisation has provided statisticians and practitioners a powerful analytical device for characterizing covariations between continuous longitudinal responses. How to develop a delineation of such an unconstrained regression framework amongst categorical or discrete longitudinal responses, however, remains an open and challenging problem. This paper studies, for the first time, a novel %representation of correlations between mean-correlation regression for a family of generic discrete responses. Targeting at the joint distributions of the discrete longitudinal responses, our regression approach is constructed by using an innovative copula model whose correlation parameters are represented by unconstrained hyperspherical coordinates. To overcome the computational intractability in maximising the full likelihood of the discrete responses in practice, we develop a computationally efficient pairwise likelihood approach for estimation. We show that the resulting estimators of the proposed approaches are consistent and asympotitcally normal. A pairwise likelihood ratio test is further proposed for statistical inference. We demonstrate the effectiveness, parsimoniousness and desirable performance of the proposed approach by analysing three discrete longitudinal data sets and conducting extensive simulations.