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.
LAGONA F, JDANOV D, & SHKOLNIKOVA M (2014). Latent time-varying factors in longitudinal analysis: a linear mixed hidden Markov model for heart rates. STATISTICS IN MEDICINE, 33(23), 4116-4134.
Titolo: | Latent time-varying factors in longitudinal analysis: a linear mixed hidden Markov model for heart rates |
Autori: | |
Data di pubblicazione: | 2014 |
Rivista: | |
Citazione: | LAGONA F, JDANOV D, & SHKOLNIKOVA M (2014). Latent time-varying factors in longitudinal analysis: a linear mixed hidden Markov model for heart rates. STATISTICS IN MEDICINE, 33(23), 4116-4134. |
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. |
Handle: | http://hdl.handle.net/11590/142600 |
Appare nelle tipologie: | 1.1 Articolo in rivista |