An Optimal Design Criterion for Within-Individual Covariance Matrices Discrimination and Parameter Estimation in Nonlinear Mixed Effects Models - López-Ríos, Victor Ignacio; Castañeda-López, María Eugenia (Revista Colombiana de Estadística, 2020)
In this paper, we consider the problem of nding optimal population designs for within-individual covariance matrices discrimination and parameter estimation in nonlinear mixed eects models. A compound optimality criterion is provided, which combines an estimation criterion and a discrimination criterion. We used the D-optimality criterion for parameter estimation, which maximizes the determinant of the Fisher information matrix. For discrimination, we propose a generalization of the T-optimality criterion for xed-eects models. Equivalence theorems are provided for these criteria. We illustrated the application of compound criteria with an example in a pharmacokinetic experiment.