LASSO-type estimators for semiparametric nonlinear mixed-effects models estimation.

Summary 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 compro-mise and retain nice features of both parametric and non-parametric models resulting in more flexible models than standard parametric NLMEs. However, SNMMs are com-plex models for which estimation still remains a challenge. Previous estimation procedures are based on a combination of log-likelihood approximation methods for parametric es-timation and smoothing splines techniques for nonparamet-ric estimation. In this work, we propose new estimation strate-gies 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 vari-Ana Arribas-Gil is supported by projects MTM2010-17323 and ECO2011-25706, Spain. Karine Bertin is supported by projects FONDECYT 1090285 and ECOS/CONICYT C10E03 2010, Chile. Cristian Meza is supported by project FONDECYT 11090024, Chile.