Model selection criteria for nonlinear mixed effects modeling

TL;DR

This paper develops a Bayesian model selection criterion for nonlinear mixed effects models using basis expansions, enabling effective evaluation and selection of model complexity based on maximum likelihood estimates.

Contribution

It introduces a novel Bayesian criterion specifically designed for nonlinear mixed effects models estimated via maximum likelihood, incorporating basis expansion techniques.

Findings

Simulation results demonstrate the effectiveness of the proposed model selection criterion.

The method successfully evaluates and compares different basis expansion models.

The approach improves model selection accuracy in nonlinear mixed effects modeling.

Abstract

We consider constructing model selection criteria for evaluating nonlinear mixed effects models via basis expansions. Mean functions and random functions in the mixed effects model are expressed by basis expansions, then they are estimated by the maximum likelihood method. In order to select numbers of basis we derive a Bayesian model selection criterion for evaluating nonlinear mixed effects models estimated by the maximum likelihood method. Simulation results shows the effectiveness of the mixed effects modeling.