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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Bibliographic Details
Published in:	Health services and outcomes research methodology Vol. 20; no. 4; pp. 222 - 236
Main Authors:	Lin, Xiaolei, Mermelstein, Robin, Hedeker, Donald
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.12.2020 Springer Nature B.V
Subjects:	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 Access:	Get full text
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