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Bayesian learners in gradient boosting for linear mixed models.

Boyao Zhang1, Colin Griesbach1, Elisabeth Bergherr1

  • 1Chair of Spatial Data Science and Statistical Learning, Georg-August-Unversität Göttingen, Göttingen, Germany.

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|December 6, 2022
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Summary
This summary is machine-generated.

BayesBoost offers a new method for mixed models, improving the selection of fixed and random effects. This approach enhances statistical inference by integrating Bayesian learning with gradient boosting for more accurate estimations.

Keywords:
Bayesian learnergradient boostinghigh-dimensionallinear mixed modelsprobingvariable selection

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Area of Science:

  • Statistics
  • Machine Learning

Background:

  • Mixed models are crucial for analyzing complex data structures.
  • Current boosting techniques face challenges with biased random effect estimates and inflexible selection.
  • Accurate selection of fixed and random effects is vital for reliable mixed model inference.

Purpose of the Study:

  • To introduce BayesBoost, a novel inference method for linear mixed models.
  • To enable simultaneous estimation and selection of fixed and random effects.
  • To address limitations in current boosting and Bayesian inference methods for mixed models.

Main Methods:

  • Integration of a Bayesian learner into gradient boosting.
  • Development of a novel selection strategy for random effects, including random slopes.
  • Simultaneous estimation and selection of fixed and random effects in high-dimensional data.

Main Results:

  • BayesBoost provides computationally fast selection of random slopes.
  • The method overcomes limitations of Bayesian inference by offering precise covariate selection guidelines.
  • It enables Bayesian construction of estimators for parameter precision, such as variance components and credible intervals.

Conclusions:

  • BayesBoost offers an effective and flexible approach for mixed model inference.
  • The method enhances the selection and estimation of fixed and random effects.
  • It provides valuable Bayesian precision estimates not found in conventional boosting frameworks.