Quantile ridge beta regression model and Bayesian regularized quantile beta regression for solving the skewness and improving the accuracy of the model selection in bounded data

Authors

  • Fedaa Noeel Abdulahad
    School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia;
    Department of Mathematics, College of Education for Pure Science, University of Al-Hamdaniya, Bartella 41006, Mosul, Iraq
  • Majid Khan Majahar Ali
    School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia
  • Raja Aqib Shamim
    School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia;
    Department of Mathematics, University of Kotli, Kotli 11100, Azad Jammu and Kashmir, Pakistan

Keywords:

Skewed data, Bayesian quantile regression, Beta distribution, Penalized quantile regression, Bounded data

Abstract

Skewed distributions have an additional shape parameter that represents the direction of the asymmetry in the density. If skewness in observations is ignored, statistical inferences based on symmetric distributions may yield biased or even misleading conclusions. Bayesian regularized quantile regression (BRQR) is effective in quantile regression for addressing skewness. Additionally, quantile ridge regression (QRR) has been developed for variable selection and to address multicollinearity among predictor variables. Most studies have used the asymmetric Laplace distribution (ALD) to address skewness, but this solution is not useful for bounded data because it has unbounded support, allowing values far in either direction and potentially violating the bounded-data assumption. The hybrid Bayesian regularized quantile beta regression--quantile ridge beta regression (BRQBR--QRBR) model is proposed for analyzing bounded data within the interval (0,1) with skewness while improving model-selection accuracy. The proposed model applies BRQBR to the skewed, bounded data to address skewness and then uses QRBR to improve model accuracy. Simulation studies and real-dataset applications were conducted. The results show that the proposed BRQBR--QRBR method, in most cases, outperforms the original method by improving prediction accuracy through skewness correction.

Dimensions

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FIG5

Published

2026-09-17

How to Cite

Quantile ridge beta regression model and Bayesian regularized quantile beta regression for solving the skewness and improving the accuracy of the model selection in bounded data. (2026). Journal of the Nigerian Society of Physical Sciences, 8(4), 3629. https://doi.org/10.46481/jnsps.2026.3629

Issue

Volume 8, Issue 4, November 2026 (in progress)

Section

Mathematics & Statistics
Copyright & Licensing

Copyright (c) 2026 Fedaa Noeel Abdulahad, Majid Khan Majahar Ali, Raja Aqib Shamim (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Quantile ridge beta regression model and Bayesian regularized quantile beta regression for solving the skewness and improving the accuracy of the model selection in bounded data. (2026). Journal of the Nigerian Society of Physical Sciences, 8(4), 3629. https://doi.org/10.46481/jnsps.2026.3629

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