High-Dimensional Bayesian Regularised Regression with the BayesReg Package

2016/11/21 by Enes Makalic, Daniel F. Schmidt, Makalic, Enes +1 · 2 citations
Computer Science · Decision Sciences · Engineering · Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference #Probabilistic and Robust Engineering Design #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1611.06649

openalex publication_date 2016/11/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Bayesian penalized regression techniques, such as the Bayesian lasso and the Bayesian horseshoe estimator, have recently received a significant amount of attention in the statistics literature. However, software implementing state-of-the-art Bayesian penalized regression, outside of general purpose Markov chain Monte Carlo platforms such as STAN, is relatively rare. This paper introduces bayesreg, a new toolbox for fitting Bayesian penalized regression models with continuous shrinkage prior densities. The toolbox features Bayesian linear regression with Gaussian or heavy-tailed error models and Bayesian logistic regression with ridge, lasso, horseshoe and horseshoe+ estimators. The toolbox is free, open-source and available for use with the MATLAB and R numerical platforms.

Cited by

• The Lasso Distribution: Properties, Sampling Methods, and Applications in Bayesian Lasso Regression
• Efficient Parameter Estimation for Bayesian Network Classifiers using Hierarchical Linear Smoothing

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