Hierarchical Mixture Models for Zero-inflated Correlated Count Data

作     者：Xue-dong CHEN Hong-xing SHI Xue-ren WANG 

作者机构：School of ScienceHuzhou UniversityHuzhou 313000China School of Primary EducationChuxiong Normal UniversityChuxiong 675000China Department of StatisticsYunnan UniversityKunming 650091China 

出 版 物：《Acta Mathematicae Applicatae Sinica》 (应用数学学报（英文版）)

年 卷 期：2016年第32卷第2期

页      面：373-384页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the National Natural Science Foundation of China(No.11171105 and No.11171293) National Social Science Foundation of China(No.10BTJ001) 

主　　题：zero-inflation random effect latent class stochastic EM algorithm model selection 

摘      要：Count data with excess zeros are often encountered in many medical, biomedical and public health applications. In this paper, an extension of zero-inflated Poisson mixed regression models is presented for dealing with multilevel data set, referred as hierarchical mixture zero-inflated Poisson mixed regression models. A stochastic EM algorithm is developed for obtaining the ML estimates of interested parameters and a model comparison is also considered for comparing models with different latent classes through BIC criterion. An application to the analysis of count data from a Shanghai Adolescence Fitness Survey and a simulation study illustrate the usefulness and effectiveness of our methodologies.