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Related Experiment Video

Updated: Jul 11, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Variable selection in linear-circular regression models.

Onur Camli1, Zeynep Kalaylioglu1, Ashis SenGupta2

  • 1Department of Statistics, Middle East Technical University, Ankara, Türkiye.

Journal of Applied Statistics
|November 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a robust Bayesian lasso method for variable selection in linear-circular regression models. The new empirical Bayes approach improves coefficient estimate stability and model complexity reduction.

Keywords:
Bayesian lassoRegularizationcircular regressiondimension reductionlaplace distribution

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Area of Science:

  • Statistics
  • Data Science

Background:

  • Circular regression models are widely used across meteorology, biology, and geology.
  • Variable selection remains a significant challenge in circular regression analysis.

Purpose of the Study:

  • To address the variable selection problem in linear-circular regression models.
  • To develop a robust Bayesian method for improved inference and model complexity reduction.

Main Methods:

  • Investigated the limitations of standard Bayesian lasso in linear-circular regression.
  • Proposed a robustified Bayesian lasso using an empirical Bayes (EB) type methodology.
  • Implemented Gibbs Sampling for hyper-prior construction for the tuning parameter.

Main Results:

  • Standard Bayesian lasso demonstrated sensitivity to hyper-prior settings, leading to non-robust inference.
  • The proposed EB-based hyper-prior construction offers computational feasibility.
  • Simulation studies confirmed that the EB-Gibbs Sampling (EB-GS) hyper-prior leads to more robust inference.

Conclusions:

  • The developed robustified Bayesian lasso provides an efficient method for variable selection in linear-circular regression.
  • The empirical Bayes approach enhances the stability and reliability of coefficient estimates.
  • This method effectively reduces model complexity while maintaining robust statistical inference.