Lasso-type estimators for Semiparametric Nonlinear Mixed-Effects Models Estimation

TL;DR

This paper introduces new estimation methods for semiparametric nonlinear mixed-effects models, combining EM algorithms for fixed effects and variance components with LASSO-type techniques for estimating nonlinear functions, enhancing flexibility and accuracy.

Contribution

It proposes a novel combined estimation procedure using EM algorithms and LASSO-type methods for SNMMs, improving estimation accuracy and model flexibility.

Findings

The LASSO-type estimator satisfies oracle inequalities.

The combined method performs well on simulated data.

Application to real data demonstrates practical effectiveness.

Abstract

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 by Ke and Wang (2001). 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. The estimation procedure proposed by Ke and Wang (2001) is 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 (Delyon et al., 1999) 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 simulated and real data.