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Source: Journal of Computational and Graphical Statistics
Resulting in 1 citation.
1. Zhang, Qiang
Jones, Alison Snow
Rijmen, Frank
Ip, Edward Hak-Sing
Multivariate Discrete Hidden Markov Models for Domain-Based Measurements and Assessment of Risk Factors in Child Development
Journal of Computational and Graphical Statistics 19,3 (September 2010): 746-765.
Also: http://pubs.amstat.org/doi/abs/10.1198/jcgs.2010.09015
Cohort(s): Children of the NLSY79, NLSY79
Publisher: American Statistical Association
Keyword(s): Alcohol Use; Behavior Problems Index (BPI); Child Development; Cognitive Ability; Home Observation for Measurement of Environment (HOME); Markov chain / Markov model; Modeling, Mixed Effects; Modeling, Random Effects; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading)

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. [ABSTRACT FROM AUTHOR]

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Bibliography Citation
Zhang, Qiang, Alison Snow Jones, Frank Rijmen and Edward Hak-Sing Ip. "Multivariate Discrete Hidden Markov Models for Domain-Based Measurements and Assessment of Risk Factors in Child Development." Journal of Computational and Graphical Statistics 19,3 (September 2010): 746-765.