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Source: Division of Public Health Sciences, Wake Forest Univ. School of Medicine
Resulting in 1 citation.
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Ip, Edward Hak-Sing Jones, Alison Snow Zhang, Qiang Rijmen, Frank |
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Mixed-effects Hidden Markov Model Working Paper, Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest University School of Medicine, September 21, 2007. Also: http://www.phs.wfubmc.edu/public/downloads/MHMM_Ip.pdf Cohort(s): Children of the NLSY79 Publisher: Wake Forest University School of Medicine Keyword(s): Behavior Problems Index (BPI); Home Observation for Measurement of Environment (HOME); Markov chain / Markov model; Modeling, Mixed Effects; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading) Permission to reprint the abstract has not been received from the publisher. In this paper, we develop a method - the Mixed-effects Hidden Markov Model (MHMM) - for analyzing multiple outcomes in a longitudinal context and for examining the covariates impact on HMM parameters. MHMM embeds a Generalized Linear Mixed Model (GLMM) into a HMM structure, and treat any one of the three sets of HMM parameters, i.e. prior probabilities, transition probabilities and conditional probabilities, as predicted variables. We present the overall likelihood function and its simplified forms, and estimate the parameters through an EM algorithm. The convergence of the algorithm and the model identifiability is also briefly discussed.MHMM is applied to a sample of young children drawn from the National Longitudinal Survey of Youth (NLSY). |
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Bibliography Citation
Ip, Edward Hak-Sing, Alison Snow Jones, Qiang Zhang and Frank Rijmen. "Mixed-effects Hidden Markov Model." Working Paper, Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest University School of Medicine, September 21, 2007. |