ArticleStatistica Sinica2019
MM ALGORITHMS FOR VARIANCE COMPONENT ESTIMATION AND SELECTION IN LOGISTIC LINEAR MIXED MODEL.
Article in Statistica Sinica, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Hierarchical selection of genetic and gene by environment interaction effects in high-dimensional mixed models.Statistical methods in medical research · 2025Review
- Smoothed simulated pseudo-maximum likelihood estimation for nonlinear mixed effects models with censored responses.Statistical methods in medical research · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Logistic linear mixed models are widely used in experimental designs and genetic analyses of binary traits. Motivated by modern applications, we consider the case of many groups of random effects, where each group corresponds to a variance component. When the number of variance components is large, fitting a logistic linear mixed model is challenging. Thus, we develop two efficient and stable minorization-maximization (MM) algorithms for estimating variance components based on a Laplace approximation of the logistic model. One of these leads to a simple iterative soft-thresholding algorithm for variance component selection using the maximum penalized approximated likelihood. We demonstrate the variance component estimation and selection performance of our algorithms by means of simulation studies and an analysis of real data.
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