Bayesian weighted composite quantile regression estimation for linear regression models with autoregressive errors

A. Aghamohammadi and M. Bahmani

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 8, 2888-2907

Abstract: Composite quantile regression methods have been shown to be effective techniques in improving the prediction accuracy. In this article, we propose a Bayesian weighted composite quantile regression estimation procedure to estimate unknown regression coefficients and autoregressive parameters in the linear regression models with autoregressive errors. A Bayesian joint hierarchical model is established using the working likelihood of the asymmetric Laplace distribution. Adaptive Lasso-penalized type priors are used on regression coefficients and autoregressive parameters of the model to conduct inference and variable selection simultaneously. A Gibbs sampling algorithm is developed to simulate the parameters from the posterior distributions. The proposed method is illustrated by some simulation studies and analyzing a real data set. Both simulation studies and real data analysis indicate that the proposed approach performs well.

Date: 2024
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DOI: 10.1080/03610926.2022.2150054

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