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MM ALGORITHMS FOR VARIANCE COMPONENT ESTIMATION AND SELECTION IN LOGISTIC LINEAR MIXED MODEL.

Liuyi Hu1, Wenbin Lu1, Jin Zhou2

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|June 12, 2020
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Summary
This summary is machine-generated.

This study introduces efficient algorithms for logistic linear mixed models with many variance components. The methods improve estimation and enable selection of these components in complex genetic analyses.

Keywords:
Generalized linear mixed model (GLMM)Laplace approximationMM algorithmvariance components selection

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Area of Science:

  • Biostatistics
  • Statistical Genetics
  • Computational Biology

Background:

  • Logistic linear mixed models are crucial for binary trait analysis in genetics and experimental designs.
  • Handling numerous variance components in these models presents significant computational challenges.

Purpose of the Study:

  • To develop efficient and stable algorithms for fitting logistic linear mixed models with many variance components.
  • To enable variance component selection in high-dimensional settings.

Main Methods:

  • Two minorization-maximization (MM) algorithms were developed using a Laplace approximation for the logistic model.
  • A soft-thresholding algorithm was derived for variance component selection via penalized approximated likelihood.

Main Results:

  • The proposed MM algorithms demonstrate efficient and stable estimation of variance components.
  • The soft-thresholding approach effectively performs variance component selection.

Conclusions:

  • The developed algorithms provide effective solutions for fitting complex logistic linear mixed models.
  • These methods are valuable for genetic analyses and experimental designs involving numerous random effects.