Testing Variance Components in Linear Mixed Modeling Using Permutation

Bibliographic Data

ID19291387
AuthorsHan Du (0000-0001-7538-7789, Department of Psychology, University of California, Los Angeles, Los Angeles, California, USA;, corresponding author), Lijuan Wang (0000-0002-2225-6483, University of Notre Dame, Notre Dame, Indiana, USA)
Year2020
Volume55
Issue1
Pages120-136
Publication date2020-01-02
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMultivariate Behavioral Research (JOURNAL)
Journal identifiersISSN: 0027-3171 • E-ISSN: 1532-7906
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00273171.2019.1627513
PMID31246110
OpenAlexW2955450754
LanguageEN
Citations received5
References cited33

Inference of variance components in linear mixed modeling (LMM) provides evidence of heterogeneity between individuals or clusters. When only nonnegative variances are allowed, there is a boundary (i.e., 0) in the variances’ parameter space, and regular inference statistical procedures for such a parameter could be problematic. The goal of this article is to introduce a practically feasible permutation method to make inferences about variance components while considering the boundary issue in LMM. The permutation tests with different settings (i.e., constrained vs. unconstrained estimation, specific vs. generalized test, different ways of calculating p values, and different ways of permutation) were examined with both normal data and non-normal data. In addition, the permutation tests were compared to likelihood ratio (LR) tests with a mixture of chi-squared distributions as the reference distribution. We found that the unconstrained permutation test with the one-sided p-value approach performed better than the other permutation tests and is a useful alternative when the LR tests are not applicable. An R function is provided to facilitate the implementation of the permutation tests, and a real data example is used to illustrate the application. We hope our results will help researchers choose appropriate tests when testing variance components in LMM

Algorithm · Boundary (topology) · Combinatorics · Inference · Multiple comparisons problem · Permutation (music) · Random permutation · Resampling · Statistical hypothesis testing · Statistics · Symmetric group · Variance (accounting) · Advanced Statistical Modeling Techniques · Artificial Intelligence · Computer Science · Mathematics · Optimal Experimental Design Methods · Statistical Methods in Clinical Trials

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