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A multilevel Ornstein–Uhlenbeck process with individual- and variable-specific estimates as random effects

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dc.contributor.authorMartínez Huertas, José Ángel
dc.contributor.authorFerrer, Emilio
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades. España
dc.date.accessioned2025-12-09T13:56:38Z
dc.date.available2025-12-09T13:56:38Z
dc.date.issued2025-12-08
dc.descriptionThe registered version of this article, first published in British Journal of Mathematical and Statistical Psychology, is available online at the publisher's website: Wiley; The British Psychological Society, https://doi.org/10.1111/bmsp.70019
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en British Journal of Mathematical and Statistical Psychology, está disponible en línea en el sitio web del editor: Wiley; The British Psychological Society, https://doi.org/10.1111/bmsp.70019
dc.description.abstractIn the present study, we extend a stochastic differential equation (SDE) model, the Ornstein–Uhlenbeck (OU) process, to the simultaneous analysis of time series of multiple variables by means of random effects for individuals and variables using a Bayesian framework. This SDE model is a stationary Gauss-Markov process that varies over time around its mean. Our extension allows us to estimate the variability of different parameters of the process, such as the mean (μ) or the drift parameter (φ), across individuals and variables of the system by means of marginalized posterior distributions. We illustrate the estimations and the interpretability of the parameters of this multilevel OU process in an empirical study of affect dynamics where multiple individuals were measured on different variables at multiple time points. We also conducted a simulation study to evaluate whether the model can recover the population parameters generating the OU process. Our results support the use of this model to obtain both the general parameters (common to all individuals and variables) and the variable-specific point estimates (random effects). We conclude that this multilevel OU process with individual- and variable-specific estimates as random effects can be a useful approach to analyse time series for multiple variables simultaneously.en
dc.description.provenanceMade available in DSpace on 2025-12-09T13:56:38Z (GMT). No. of bitstreams: 1 A multilevel Ornstein–Uhlenbeck process.pdf: 536851 bytes, checksum: 063a6b74c1c2eebfbe7fe7001ccface6 (MD5) Previous issue date: 2025-12-08en
dc.description.sponsorshipJAM-H was funded by the Spanish Ministry of Universities within the framework of the State Program to Develop, Attract and Retain Talent, State Mobility subprogram of the State Plan for Scientific, Technical and Innovation Research 2021–2023 (ref. CAS22/00291) to develop this work.
dc.description.versionversión publicada
dc.identifier.citationMartínez-Huertas, J. Á., & Ferrer, E. (2025). A multilevel Ornstein–Uhlenbeck process with individual- and variable-specific estimates as random effects. British Journal of Mathematical and Statistical Psychology, 00, 1–16. https://doi.org/10.1111/bmsp.70019
dc.identifier.doihttps://doi.org/10.1111/bmsp.70019
dc.identifier.issn2044-8317
dc.identifier.urihttps://hdl.handle.net/20.500.14468/31053
dc.journal.titleBritish Journal of Mathematical and Statistical Psychology
dc.language.isoen
dc.publisherWiley; The British Psychological Society
dc.relation.centerFacultad de Psicología
dc.relation.departmentMetodología de las Ciencias del Comportamiento
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject6107.01 Metodología
dc.subject6302.02 Psicología social
dc.subject.keywordsOU processen
dc.subject.keywordsstochastic differential equationsen
dc.subject.keywordsmultilevelen
dc.subject.keywordsrandom effectsen
dc.subject.keywordsmultivariate time seriesen
dc.subject.keywordsaffect dynamicsen
dc.titleA multilevel Ornstein–Uhlenbeck process with individual- and variable-specific estimates as random effectsen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationca510876-0be8-438a-a565-ac5f8953fb78
relation.isAuthorOfPublication.latestForDiscoveryca510876-0be8-438a-a565-ac5f8953fb78
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