  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.
