A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure

• 成果类型: Article
• 署名作者: Bartolucci, Francesco; Farcomeni, Alessio
• 署名单位: University of Perugia; Sapienza University Rome
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.0107
• 发表日期: 2009
• 页码: 816-831
• 关键词: regression-models; likelihood inference; maximum-likelihood; association; bivariate

摘要

For the analysis of multivariate categorical longitudinal data, we propose an extension of the dynamic logit model. The resulting model is based oil a marginal parameterization of the conditional distribution of each vector of response variables given the covariates, the lagged response variables. and a set of subject-specific parameters for the unobserved heterogeneity. The latter ones are assumed to follow a first-order Markov chain. For the maximum likelihood estimation of the model parameters, we outline an EM algorithm. The data analysis approach based on the Proposed model is illustrated by a simulation study and in application to a dataset. which derives front the Panel Study on Income Dynamics and concerns fertility and female participation to the labor market.