Mixed location scale hidden Markov model for the analysis of intensive longitudinal data

Hidden Markov models (HMM) presents an attractive analytical framework for capturing the state-switching process for auto-correlated data. These models have been extended to longitudinal data setting where simultaneous multiple processes are observed by including subject specific random effects. How...

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Veröffentlicht in:	Health services and outcomes research methodology Jg. 20; H. 4; S. 222 - 236
Hauptverfasser:	Lin, Xiaolei, Mermelstein, Robin, Hedeker, Donald
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2020 Springer Nature B.V
Schlagworte:	Behavior Data compression Economics Health Administration Health services Latent class analysis Longitudinal studies Markov analysis Medicine Medicine & Public Health Methodology of the Social Sciences Probability Public Health Research methodology Statistics Teenagers Mixed effect models Intensive longitudinal data Latent class classification
ISSN:	1387-3741, 1572-9400
Online-Zugang:	Volltext
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