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A boosting method to select the random effects in linear mixed models.

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This study introduces a new boosting method for selecting random effects in linear mixed models. The approach effectively handles complex objective functions, demonstrating strong performance in simulations and real-world data analysis.

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Area of Science:

  • Statistics
  • Statistical Modeling

Background:

  • Linear mixed models are widely used in various scientific fields.
  • Selecting appropriate random effects is crucial for model accuracy.
  • Existing methods face challenges with non-convex objective functions.

Purpose of the Study:

  • To propose a novel likelihood-based boosting method for random effects selection.
  • To address the challenges posed by non-convex objective functions in model optimization.

Main Methods:

  • Developed a boosting algorithm utilizing likelihood-based criteria.
  • Incorporated directions of negative curvature alongside Newton directions for optimization.
  • Applied the method to a simulated dataset and a real-world application.

Main Results:

  • The proposed method demonstrates effective selection of random effects.
  • The optimization strategy successfully navigates the non-convex objective function.
  • Both simulation and real-data results confirm the method's good performance.

Conclusions:

  • The novel likelihood-based boosting method offers a robust solution for random effects selection.
  • The optimization technique enhances the reliability of fitting linear mixed models.
  • This approach provides a valuable tool for statistical modeling and data analysis.