 Varying-coefficient hidden Markov models with zero-effect regionsHefei Liu, Xinyuan Song and Baoxue Zhang Computational Statistics & Data Analysis, 2022, vol. 173, issue C Abstract: In psychological, social, behavioral, and medical studies, hidden Markov models (HMMs) have been extensively applied to the simultaneous modeling of longitudinal observations and the underlying dynamic transition process. However, the existing HMMs mainly focus on constant-coefficient HMMs. This study considers a varying-coefficient HMM, which enables simultaneous investigation of the dynamic covariate effects and between-state transitions. Moreover, a soft-thresholding operator is introduced to detect zero-effect regions of the coefficient functions. A full Bayesian approach with a hybird Markov chain Monte Carlo algorithm that combines B-spline approximation and penalization technique is developed for statistical inference. The empirical performance of the propose method is evaluated through simulation studies. An application to a study on the Alzheimer's Disease Neuroimaging Initiative dataset is presented. Keywords: Bayesian method; Longitudinal data; Spline approximation; Varying-coefficient models; Zero-effect regions (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:173:y:2022:i:c:s0167947322000627 DOI: 10.1016/j.csda.2022.107482 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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