讲座题目：Grouped Dirichlet Distribution: A New Tool for Incomplete Categorical Data Analysis

主讲人: 田国梁, Division of Biostatistics, University of Maryland Greenebaum Cancer Center

时   间: 2008.6.9, 14:30-15:30 

地   点: F203, 四维创新实验室

摘   要: Motivated by the likelihood functions of several incomplete categorical data, this article introduces a new family of distributions, grouped Dirichlet distributions (GDD), which includes the classical Dirichlet distribution (DD) as a special case. First, we develop distribution theory for the GDD in its own right. Second, we use this expanded family as a new tool for statistical analysis of incomplete categorical data. Starting with a GDD with two partitions, we derive its stochastic representation that provides a simple procedure for simulation. Other properties such as mixed moments, mode, marginal and conditional distributions are also derived. The general GDD with more than two partitions is considered in a parallel manner. Three data sets from a case-control study, a leprosy survey, and a neurological study are used to illustrate how the GDD can be used as a new tool for analyzing incomplete categorical data. Our approach based on GDD has at least two advantages over the commonly used approach based on the DD in both frequentist and conjugate Bayesian inference: (a) In some cases, both the maximum likelihood and Bayes estimates have closed-form expressions in the new approach, but not so when they are based on the commonly-used approach; and (b) even if a closed-form solution is not available, the EM and data augmentation algorithms in the new approach converge much faster than in the commonly-used approach.　

　

主讲人简介:
Dr. Guo-Liang Tian is a senior biostatistician and faculty member in the division of Biostatistics, University of Maryland Greenebaum Cancer Center. His research interest covers areas of Incomplete categorical data analysis, Constrained parameter models and Variables selection, Sample surveys with sensitive questions, Non-iterative Monte Carlo methods, IBF Sampler and MCMC, Bayesian Analysis in Medicine, EM and MM algorithms, Reliability, prediction inferences and finance statistics and so on.