文章基本信息

标题：Bayesian zero-inflated growth mixture models with application to health risk behavior data
本地全文：下载
作者：Yang, Si ; Puggioni, Gavino
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2021
卷号：14
期号：2
页码：151-163
DOI：10.4310/20-SII623
出版社：International Press
摘要：This paper focuses on developing latent class models for longitudinal data with zero-inflated count response variables. The goals are to model discrete longitudinal patterns of rare events counts (for instance, health-risky behavior), and to identify individual-specific covariates associated with latent class probabilities. Two discrete latent structures are present in this type of model: a latent categorical variable that classifies subgroups with distinct developmental trajectories and a latent binary variable that identifies whether an observation is from a zero-inflation process or a regular count process. Within each class, two sets of covariates are used to separately model the probability of structural zeros and the mean trajectories of the count process. The estimation of the latent variables and regression parameters are carried jointly in a hierarchical Bayesian framework. Our methods are validated through a simulation study and then applied to cigarette smoking data, obtained from the National Longitudinal Study of Adolescent Health.
关键词：latent class models; finite mixtures; longitudinal methods; adolescent smoking; behavioral sciences; hierarchical models