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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Joint variable selection for fixed and random effects in linear mixed-effects models.

Howard D Bondell1, Arun Krishna, Sujit K Ghosh

  • 1Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, USA. bondell@stat.ncsu.edu

Biometrics
|February 19, 2010
PubMed
Summary

This study introduces a new method for simultaneously selecting important predictors in linear mixed-effects (LME) models. The approach ensures better model accuracy by considering both fixed and random effects together.

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Area of Science:

  • Statistics
  • Statistical Modeling

Background:

  • Traditional methods for linear mixed-effects (LME) models often select fixed and random effects separately.
  • Separate selection can lead to suboptimal model structures as the choice of variables for one component impacts the other.

Purpose of the Study:

  • To develop a method for simultaneous selection of fixed and random effects in LME models.
  • To improve the accuracy and efficiency of variable selection in complex statistical models.

Main Methods:

  • A novel approach using a modified Cholesky decomposition for simultaneous selection.
  • A penalized joint log-likelihood function with an adaptive penalty is employed.
  • Model selection is achieved by allowing effects or standard deviations to be exactly zero, utilizing a constrained expectation-maximization algorithm.

Main Results:

  • The proposed penalized estimator demonstrates the Oracle property, achieving asymptotic performance equivalent to knowing the true model.
  • Simultaneous selection leads to more robust and accurate identification of important predictors.

Conclusions:

  • The developed method provides an effective way to simultaneously select fixed and random effects in LME models.
  • This approach offers significant advantages over traditional separate selection methods, validated by simulations and real-world data.