Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

High-Dimensional Variable Selection for Quantile Regression based on Variational Bayesian Method

Version 1 : Received: 14 April 2023 / Approved: 14 April 2023 / Online: 14 April 2023 (09:36:47 CEST)

A peer-reviewed article of this Preprint also exists.

Dai, D.; Tang, A.; Ye, J. High-Dimensional Variable Selection for Quantile Regression Based on Variational Bayesian Method. Mathematics 2023, 11, 2232. Dai, D.; Tang, A.; Ye, J. High-Dimensional Variable Selection for Quantile Regression Based on Variational Bayesian Method. Mathematics 2023, 11, 2232.

Abstract

Quantile regression model is widely used in variable relationship research of general size data, due to strong robustness and more comprehensive description of the response variables' characteristics. With the increase of data size and data dimension, there have been some studies on high-dimensional quantile regression under the classical statistical framework, including higher-efficient frequency perspective, which is however at cost of randomness quantification, or lower-efficient Bayesian method based on MCMC sampling. To overcome these problems, we propose the high-dimensional quantile regression with Spike-and-Slab Lasso penalty based on variational Bayesian (VBSSLQR), which can not only improve the computational efficiency but also measure the randomness via variational distributions. The simulation studies and real data analysis illustrate that the proposed VBSSLQR method is superior to or equivalent to other quantile and non-quantile regression methods (including Bayesian and non-Bayesian methods), and its efficiency is higher than any other method.

Keywords

quantile regression; Spike-and-Slab prior; variational Bayesian; high-dimensional data

Subject

Computer Science and Mathematics, Probability and Statistics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.