Likelihood ratio test for variance components in nonlinear mixed effects models

TL;DR

This paper develops a general likelihood ratio test for variance components in both linear and nonlinear mixed effects models, establishing its asymptotic chi-bar-square distribution and demonstrating its effectiveness through simulations and real data applications.

Contribution

It extends existing results by providing a comprehensive testing procedure for variance components in general mixed effects models, including nonlinear cases.

Findings

The asymptotic distribution is a chi-bar-square, depending on correlations between random effects.

The test performs well in finite samples as shown by simulations.

Application to real datasets demonstrates practical utility.

Abstract

Mixed effects models are widely used to describe heterogeneity in a population. A crucial issue when adjusting such a model to data consists in identifying fixed and random effects. From a statistical point of view, it remains to test the nullity of the variances of a given subset of random effects. Some authors have proposed to use the likelihood ratio test and have established its asymptotic distribution in some particular cases. Nevertheless, to the best of our knowledge, no general variance components testing procedure has been fully investigated yet. In this paper, we study the likelihood ratio test properties to test that the variances of a general subset of the random effects are equal to zero in both linear and nonlinear mixed effects model, extending the existing results. We prove that the asymptotic distribution of the test is a chi-bar-square distribution, that is to say a mixture of chi-square distributions, and we identify the corresponding weights. We highlight in particular that the limiting distribution depends on the presence of correlations between the random effects but not on the linear or nonlinear structure of the mixed effects model. We illustrate the finite sample size properties of the test procedure through simulation studies and apply the test procedure to two real datasets of dental growth and of coucal growth.