School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Bayesian variable selection and estimation in binary quantile regression using global-local shrinkage priors

Document Type

Article

Publication Date

12-11-2023

Abstract

In this paper, we construct a Bayesian hierarchical model with global-local shrinkage priors for the regression coefficients, which includes the horseshoe prior and normal-gamma prior. This model is used for high-dimensional quantile regression models with dichotomous response data. We have developed an efficient sampling algorithm to generate posterior samplings for making posterior inference. We use a location-scale mixture representation of the asymmetric Laplace distribution. We assess the performance of the proposed methods through Monte Carlo simulations and two real-data applications in terms of parameter estimation and variable selection. Numerical results demonstrate that the proposed methods perform comparably with existing Bayesian methods under a variety of scenarios.

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© 2023 Taylor & Francis Group, LLC.

https://www.tandfonline.com/share/YQKXUCF9IGNXRXSXEHAT?target=10.1080/03610918.2023.2293638

Publication Title

Communications in Statistics - Simulation and Computation

DOI

10.1080/03610918.2023.2293638

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