Cross-validation for nonlinear mixed effects models

Model Building for Nonlinear Mixed-Effects Models

Nonlinear mixed-effects models involve both fixed effects and random effects. Model building for nonlinear mixed-effects models is the process of determining the characteristics of both the fixed and the random effects so as to give an adequate but parsimonious model. We describe procedures based on information criterion statistics for comparing different structures of the random effects compon...

Nonlinear mixed effects models for longitudinal data

Generalized Cross-Validation for Wavelet Shrinkage in Nonparametric Mixed-Effects Models

A nonlinear wavelet shrinkage estimator was proposed in Huang and Lu (2000). Such an estimator combined the asymptotic equivalence to the best linear unbiased prediction and the Bayesian estimation in nonparametric mixed-effects models. In this article a data-driven GCV method is proposed to select hyperparameters. The proposed GCV method has low computational cost and can be applied to one or ...

Dirichlet Processes in Nonlinear Mixed Effects Models

Two efficient approaches are considered to deal with the difficulty in computing the intractable integrals when implementing Gibbs sampling in the nonlinear mixed effects model (NLMM) based on Dirichlet processes (DP). The first approach computes the Laplace's approximation to the integral for its high accuracy, low cost, and ease of implementation. The second approach uses the no-gaps algorith...

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

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 ...