A Monte Carlo simulation is employed to investigate the performance of five estimation methods of nonlinear mixed effects models in terms of parameter recovery and efficiency of both regression coefficients and variance/covariance parameters under varying levels of data sparseness and model misspecification.
Harring, Jeffrey R. and Liu, Junhui
"A Comparison of Estimation Methods for Nonlinear Mixed-Effects Models Under Model Misspecification and Data Sparseness: A Simulation Study,"
Journal of Modern Applied Statistical Methods:
1, Article 27.
Available at: http://digitalcommons.wayne.edu/jmasm/vol15/iss1/27