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LASSO-type estimators for semiparametric nonlinear mixed-effects models estimation / Ana Arribas-Gil in Statistics and Computing, vol 24 n° 3 (May 2014)  

Public [article]  inStatistics and Computing > vol 24 n° 3 (May 2014) . - pp 443 - 460 Titre : LASSO-type estimators for semiparametric nonlinear mixed-effects models estimation Type de document : Article/Communication Auteurs : Ana Arribas-Gil, Auteur ; Karine Bertin, Auteur ; Christian Meza, Auteur ; Vincent Rivoirard, Auteur Année de publication : 2014 Article en page(s) : pp 443 - 460 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] estimation statistique [Termes IGN] modèle non linéaire Résumé : (auteur) Parametric nonlinear mixed effects models (NLMEs) are now widely used in biometrical studies, especially in pharmacokinetics research and HIV dynamics models, due to, among other aspects, the computational advances achieved during the last years. However, this kind of models may not be flexible enough for complex longitudinal data analysis. Semiparametric NLMEs (SNMMs) have been proposed as an extension of NLMEs. These models are a good compromise and retain nice features of both parametric and nonparametric models resulting in more flexible models than standard parametric NLMEs. However, SNMMs are complex models for which estimation still remains a challenge. Previous estimation procedures are based on a combination of log-likelihood approximation methods for parametric estimation and smoothing splines techniques for nonparametric estimation. In this work, we propose new estimation strategies in SNMMs. On the one hand, we use the Stochastic Approximation version of EM algorithm (SAEM) to obtain exact ML and REML estimates of the fixed effects and variance components. On the other hand, we propose a LASSO-type method to estimate the unknown nonlinear function. We derive oracle inequalities for this nonparametric estimator. We combine the two approaches in a general estimation procedure that we illustrate with simulations and through the analysis of a real data set of price evolution in on-line auctions. Numéro de notice : A2014-790 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11222-013-9380-x Date de publication en ligne : 07/02/2013 En ligne : http://dx.doi.org/10.1007/s11222-013-9380-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79157 [article]