title: Multivariate Discrete Hidden Markov Models for Domain-Based Measurements and Assessment of Risk Factors in Child Development.
abstract: Many studies in the social and behavioral sciences involve multivariate discrete measurements, which are often characterized by the presence of an underlying individual trait, the existence of clusters such as domains of measurements, and the availability of multiple waves of cohort data. Motivated by an application in child development, we propose a class of extended multivariate discrete hidden Markov models for analyzing domain-based measurements of cognition and behavior. A random effects model is used to capture the long-term trait. Additionally, we develop a model selection criterion based on the Bayes factor for the extended hidden Markov model. The National Longitudinal Survey of Youth (NLSY) is used to illustrate the methods. Supplementary technical details and computer codes are available online.
authors.affiliation: Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27159.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Business and Public Health Partnerships, Drexel University School of Public Health, Philadelphia, PA 19102.
authors.forename: Alison
authors.initials: A
authors.lastname: Snow Jones
authors.affiliation: Educational Testing Service, Princeton, NJ 08541.
authors.forename: Frank
authors.initials: F
authors.lastname: Rijmen
authors.affiliation: Department of Biostatistical Sciences and Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC 27159 ( eip@wfubmc.edu ).
authors.forename: Edward H
authors.initials: EH
authors.lastname: Ip
keywords: Junction tree; Mixed effects; National Longitudinal Survey of Youth