ResearchStats: Gavino Puggioni
Date and Time
November 10, 2015
12:00PM - 01:00PM EST
Location
Science Center Rm. 705
Title: A Bayesian Zero-inflated Latent class Model for Longitudinal Data
Abstract: This work focuses on developing latent class models for longitudinal data with zero-inflated count response variables. The goals are to model discrete, longitudinal patterns of counts of rare events (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 predictors 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.
Abstract: This work focuses on developing latent class models for longitudinal data with zero-inflated count response variables. The goals are to model discrete, longitudinal patterns of counts of rare events (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 predictors 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.