中国人民大学统计学院

讲座题目：

Flexible generalized t-link models for binary response data

主讲人：

Professor Ming-Hui Chen

Department of Statistics, University of Connecticut, Storrs, CT 06269, USA

讲座时间:

June 25th 2008(Wed) 3:30pm-4:30pm

讲座地点

见学院公告栏

讲座摘要：

A critical issue in modelling binary response data is the choice of the links.We introduce a new link based on the generalized t-distribution. There are two parameters in the generalized t-link: one parameter purely controls the heaviness of the tails of the link and the second parameter controls the scale of the link. Two major advantages are offered by the generalized t-links. First, a symmetric generalized t-link with an unknown shape parameter is much more identifiable than a Student t-link with unknown degrees of freedom and a known scale parameter. Secondly, skewed generalized t-links with both unknown shape and scale parameters provide much more flexible and improved skewed link regression models than the existing skewed links.Various theoretical properties and attractive features of the proposed links are examined and explored in details.An efficient Markov chain Monte Carlo algorithm is developed for sampling from the posterior distribution. The Deviance Information Criteria measure is used for guiding the choice of links. The proposed methodology is motivated and illustrated by prostate cancer data.

主讲人简介：

Dr. Ming-Hui Chen got his Ph.D degree in 1993 from Purdue University. He is now a full professor at Department of Statistics, University of Connecticut. He was elected as a Fellow of the Institute of Mathematical Statistics in 2007. and a Fellow of the American Statistical Association in 2005. He was elected as an ordinary member of the International Statistical Institute (ISI)in 1999.He received the Harold J. Gay Professorship in Mathematical Sciences from WPI, 1998-2000, and the I.W. Burr Award in Statistics from Purdue University in 1993. He is also members of many professional societies, including Institute of Mathematical Statistics, American Statistical Association; ENAR, The International Biometric Society; Section on Bayesian Statistics; International Chinese Statisticians Association; and The International Statistical Institute.