Robust Variable Selection via Bayesian LASSO-Composite Quantile Regression with Empirical Likelihood: A Hybrid Sampling Approach

Robust Variable Selection via Bayesian LASSO-Composite Quantile Regression with Empirical Likelihood: A Hybrid Sampling Approach

Since the advent of composite quantile regression (CQR), its inherent robustness has established it as a pivotal methodology for high-dimensional data analysis. High-dimensional outlier contamination refers to data scenarios where the number of observed dimensions (<i>p</i>) is much grea...

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Bibliographic Details
Main Authors: Ruisi Nan, Jingwei Wang, Hanfang Li, Youxi Luo
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Mathematics
Subjects:
composite quantile regression
empirical likelihood
Bayesian LASSO penalty
hybrid Gibbs sampling algorithm
Online Access:https://www.mdpi.com/2227-7390/13/14/2287
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