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• W2112952709 abstract "Longitudinal data are often segmented by unobserved time-varying factors, which introduce latent heterogeneity at the observation level, in addition to heterogeneity across subjects. We account for this latent structure by a linear mixed hidden Markov model. It integrates subject-specific random effects and Markovian sequences of time-varying effects in the linear predictor. We propose an expectationŰ-maximization algorithm for maximum likelihood estimation, based on data augmentation. It reduces to the iterative maximization of the expected value of a complete likelihood function, derived from an augmented dataset with case weights, alternated with weights updating. In a case study of the Survey on Stress Aging and Health in Russia, the model is exploited to estimate the influence of the observed covariates under unobserved time-varying factors, which affect the cardiovascular activity of each subject during the observation period. Copyright © 2014 John Wiley & Sons, Ltd." @default.
• W2112952709 created "2016-06-24" @default.
• W2112952709 creator A5024611156 @default.
• W2112952709 creator A5042519142 @default.
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• W2112952709 date "2014-06-02" @default.
• W2112952709 modified "2023-10-17" @default.
• W2112952709 title "Latent time-varying factors in longitudinal analysis: a linear mixed hidden Markov model for heart rates" @default.
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• W2112952709 doi "https://doi.org/10.1002/sim.6220" @default.
• W2112952709 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4159441" @default.
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• W2112952709 hasPublicationYear "2014" @default.
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