High-Dimensional Bayesian Regularised Regression with the BayesReg Package
From MaRDI portal
Publication:128392
DOI10.48550/ARXIV.1611.06649arXiv1611.06649MaRDI QIDQ128392
Daniel F. Schmidt, Enes Makalic
Publication date: 21 November 2016
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.
Related Items (1)
This page was built for publication: High-Dimensional Bayesian Regularised Regression with the BayesReg Package
