Hierarchical-likelihood approach for nonlinear mixed-effects models

Citations of this article
Mendeley users who have this article in their library.
Get full text


The restricted maximum likelihood (REML) procedure is useful for inferences about variance components in linear mixed models (LMMs). However, its extension to nonlinear mixed models (NLMMs) is often hampered by analytically intractable integrals. For NLMMs various estimation methods have been suggested, but they have suffered from unsatisfactory biases. In this paper we propose a statistically and computationally efficient REML procedure, based upon hierarchical likelihood. Numerical studies show that the proposed method reduces the biases in the existing methods that we studied. We also study how the current REML procedure for LMMs can be modified to compute the proposed estimators. © 2007 Elsevier Ltd. All rights reserved.




Noh, M., & Lee, Y. (2008). Hierarchical-likelihood approach for nonlinear mixed-effects models. Computational Statistics and Data Analysis, 52(7), 3517–3527. https://doi.org/10.1016/j.csda.2007.10.026

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free