Model selection criteria for 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.