MM algorithms for variance component estimation and selection in logistic linear mixed model

Liuyi Hu, Wenbin Lu, Jin Zhou, Hua Zhou

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)1585-1605
Number of pages21
JournalStatistica Sinica
Volume29
Issue number3
DOIs
StatePublished - 2019

Keywords

  • Generalized linear mixed model (GLMM)
  • Laplace approximation
  • MM algorithm
  • Variance components selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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