MM Algorithms for Variance Component Estimation and Selection in Logistic Linear Mixed Model

TL;DR

This paper introduces two efficient MM algorithms for estimating and selecting variance components in logistic linear mixed models, especially when dealing with many random effects, demonstrated through simulations and real data.

Contribution

The paper develops novel MM algorithms that improve variance component estimation and selection in complex logistic mixed models with numerous random effects.

Findings

Algorithms perform well in simulation studies.

Effective variance component selection via soft-thresholding.

Validated on real genetic data.

Abstract

Logistic linear mixed model is widely used in experimental designs and genetic analysis with binary traits. Motivated by modern applications, we consider the case with many groups of random effects and each group corresponds to a variance component. When the number of variance components is large, fitting the logistic linear mixed model is challenging. We develop two efficient and stable minorization-maximization (MM) algorithms for the estimation of variance components based on the Laplace approximation of the logistic model. One of them leads to a simple iterative soft-thresholding algorithm for variance component selection using maximum penalized approximated likelihood. We demonstrate the variance component estimation and selection performance of our algorithms by simulation studies and a real data analysis.