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Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorArribas Gil, Ana
dc.contributor.authorDe La Cruz, Rolando
dc.contributor.authorLebarbier, Emilie
dc.contributor.authorMeza, Cristian
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2021-07-01T10:27:07Z
dc.date.available2021-07-01T10:27:07Z
dc.date.issued2015-06-01
dc.description.abstractWe propose a classification method for longitudinal data. The Bayes classifier is classically used to determine a classification rule where the underlying density in each class needs to be well modeled and estimated. This work is motivated by a real dataset of hormone levels measured at the early stages of pregnancy that can be used to predict normal versus abnormal pregnancy outcomes. The proposed model, which is a semiparametric linear mixed-effects model (SLMM), is a particular case of the semiparametric nonlinear mixed-effects class of models (SNMM) in which finite dimensional (fixed effects and variance components) and infinite dimensional (an unknown function) parameters have to be estimated. In SNMM’s maximum likelihood estimation is performed iteratively alternating parametric and nonparametric procedures. However, if one can make the assumption that the random effects and the unknown function interact in a linear way, more efficient estimation methods can be used. Our contribution is the proposal of a unified estimation procedure based on a penalized EM-type algorithm. The Expectation and Maximization steps are explicit. In this latter step, the unknown function is estimated in a nonparametric fashion using a lasso-type procedure. A simulation study and an application on real data are performed.en
dc.description.sponsorshipThe authors are grateful to two anonymous referees and an Associate Editor for their insightful comments and valuable suggestions, which led to substantial improvements in the presentation of this work. Ana Arribas–Gil was supported by projects MTM2010-17323 and ECO2011-25706, Spain. Rolando de la Cruz was supported by project FONDECYT 1120739, grant ANILLO ACT–87, and grant FONDAP 15130011, Chile. Cristian Meza was supported by projects FONDECYT 11090024 and 1141256, and grant ANILLO ACT–1112, CONICYT-PIA, Chile.en
dc.identifier.bibliographicCitationArribas-Gil, A., De la Cruz, R., Lebarbier, E., & Meza, C. (2015). Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators. Biometrics, 71(2), 333–343
dc.identifier.doihttps://doi.org/10.1111/biom.12280
dc.identifier.issn0006-341X
dc.identifier.publicationfirstpage333
dc.identifier.publicationissue2
dc.identifier.publicationlastpage343
dc.identifier.publicationtitleBiometrics
dc.identifier.publicationvolume71
dc.identifier.urihttps://hdl.handle.net/10016/32971
dc.identifier.uxxiAR/0000021513
dc.language.isoeng
dc.publisherWiley
dc.relation.projectIDGobierno de España. MTM2010-17323
dc.relation.projectIDGobierno de España. ECO2011-25706
dc.rights© 2015, The International Biometric Society
dc.rights.accessRightsopen access
dc.subject.ecienciaEstadísticaes
dc.subject.otherClassificationen
dc.subject.otherEM algorithmen
dc.subject.otherLassoen
dc.subject.otherLongitudinal dataen
dc.subject.otherMixed-effects modelsen
dc.subject.otherSemiparametric estimationen
dc.titleClassification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimatorsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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