Recycled Two-Stage Estimation in Nonlinear Mixed Effects Regression Models

TL;DR

This paper introduces a recycled two-stage estimation method using random weighting for nonlinear mixed effects models, providing consistent and asymptotically normal estimates of sampling distributions, applicable in clinical pharmacokinetics.

Contribution

It proposes a novel recycling approach for two-stage estimates in nonlinear mixed effects models, enhancing estimation accuracy and inference reliability.

Findings

The method achieves asymptotic consistency and normality.

Simulation studies validate the approach's effectiveness.

Application examples demonstrate practical utility.

Abstract

We consider a re-sampling scheme for estimation of the population parameters in the mixed effects nonlinear regression models of the type use for example in clinical pharmacokinetics, say. We provide an estimation procedure which {\it recycles}, via random weighting, the relevant two-stage parameters estimates to construct consistent estimates of the sampling distribution of the various estimates. We establish the asymptotic consistency and asymptotic normality of the resampled estimates and demonstrate the applicability of the {\it recycling} approach in a small simulation study and via example.