Quantile mixed hidden Markov models for multivariate longitudinal data: an application to children's Strengths and Difficulties Questionnaire scores
Quantile mixed hidden Markov models for multivariate longitudinal data: an application to children's Strengths and Difficulties Questionnaire scores
The identification of factors associated with mental and behavioural disorders in early childhood is critical both for psychopathology research and the support of primary health care practices. Motivated by the Millennium Cohort Study, in this paper we study the effect of a comprehensive set of covariates on children's emotional and behavioural trajectories in England. To this end, we develop a quantile mixed hidden Markov model for joint estimation of multiple quantiles in a linear regression setting for multivariate longitudinal data. The novelty of the proposed approach is based on the multivariate asymmetric Laplace distribution which allows to jointly estimate the quantiles of the univariate conditional distributions of a multivariate response, accounting for possible correlation between the outcomes. Sources of unobserved heterogeneity and serial dependency due to repeated measures are modelled through the introduction of individual-specific, time-constant random coefficients and time-varying parameters evolving over time with a Markovian structure respectively. The inferential approach is carried out through the construction of a suitable expectation–maximization algorithm without parametric assumptions on the random effects distribution.
EM algorithm, finite mixtures, multivariate asymmetric Laplace distribution, non-parametric maximum likelihood, quantile regression, random effects model
417-448
Merlo, Luca
436fb4df-938c-4b5d-aedc-d68e85390a36
Petrella, Lea
bf351458-2a5a-452e-be73-496a19c4060a
Tzavidis, Nikolaos
431ec55d-c147-466d-9c65-0f377b0c1f6a
March 2022
Merlo, Luca
436fb4df-938c-4b5d-aedc-d68e85390a36
Petrella, Lea
bf351458-2a5a-452e-be73-496a19c4060a
Tzavidis, Nikolaos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Merlo, Luca, Petrella, Lea and Tzavidis, Nikolaos
(2022)
Quantile mixed hidden Markov models for multivariate longitudinal data: an application to children's Strengths and Difficulties Questionnaire scores.
Journal of the Royal Statistical Society, Series C (Applied Statistics), 71 (2), .
(doi:10.1111/rssc.12539).
Abstract
The identification of factors associated with mental and behavioural disorders in early childhood is critical both for psychopathology research and the support of primary health care practices. Motivated by the Millennium Cohort Study, in this paper we study the effect of a comprehensive set of covariates on children's emotional and behavioural trajectories in England. To this end, we develop a quantile mixed hidden Markov model for joint estimation of multiple quantiles in a linear regression setting for multivariate longitudinal data. The novelty of the proposed approach is based on the multivariate asymmetric Laplace distribution which allows to jointly estimate the quantiles of the univariate conditional distributions of a multivariate response, accounting for possible correlation between the outcomes. Sources of unobserved heterogeneity and serial dependency due to repeated measures are modelled through the introduction of individual-specific, time-constant random coefficients and time-varying parameters evolving over time with a Markovian structure respectively. The inferential approach is carried out through the construction of a suitable expectation–maximization algorithm without parametric assumptions on the random effects distribution.
Text
Royal Stata Society Series C - 2022 - Merlo - Quantile mixed hidden Markov models for multivariate longitudinal data An
- Version of Record
More information
Accepted/In Press date: 9 December 2021
e-pub ahead of print date: 22 January 2022
Published date: March 2022
Keywords:
EM algorithm, finite mixtures, multivariate asymmetric Laplace distribution, non-parametric maximum likelihood, quantile regression, random effects model
Identifiers
Local EPrints ID: 454432
URI: http://eprints.soton.ac.uk/id/eprint/454432
ISSN: 0035-9254
PURE UUID: 5c400a89-8a77-4015-be99-8f7b668d3887
Catalogue record
Date deposited: 09 Feb 2022 17:38
Last modified: 17 Mar 2024 02:54
Export record
Altmetrics
Contributors
Author:
Luca Merlo
Author:
Lea Petrella
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics