Empirical Likelihood Inference of Variance Components in Linear Mixed-Effects Models
J. Zhang, W. Guo, J.S. Carpenter, Andrew Leroux, K.R. Merikangas, N.G., Martin, I.B. Hickie, H. Shou, and H. Li

TL;DR
This paper introduces empirical likelihood methods for variance component inference in linear mixed-effects models, providing a nonparametric approach that improves error control when distributional assumptions are violated.
Contribution
It develops empirical likelihood-based inference techniques for variance components in mixed-effects models, including a nonparametric Wilks' theorem and tests for multiple components.
Findings
Proposed methods outperform likelihood ratio tests under non-Gaussian conditions.
Applied to twin study data, identified heritability in specific quantile ranges.
Demonstrated better type 1 error control in simulations.
Abstract
Linear mixed-effects models are widely used in analyzing repeated measures data, including clustered and longitudinal data, where inferences of both fixed effects and variance components are of importance. Unlike the fixed effect inference that has been well studied, inference on the variance components is more challenging due to null value being on the boundary and the nuisance parameters of the fixed effects. Existing methods often require strong distributional assumptions on the random effects and random errors. In this paper, we develop empirical likelihood-based methods for the inference of the variance components in the presence of fixed effects. A nonparametric version of the Wilks' theorem for the proposed empirical likelihood ratio statistics for variance components is derived. We also develop an empirical likelihood test for multiple variance components related to a sequence…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStatistical Methods and Bayesian Inference · Statistical Methods and Inference · Advanced Causal Inference Techniques
