High-Dimensional Bayesian Regularised Regression with the BayesReg Package
arXiv:1611.06649
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.
17 pages, 1 figure
References in corpus (6)
- Nearly unbiased variable selection under minimax concave penalty
- Elliptical slice sampling
- A simple sampler for the horseshoe estimator
- Projection predictive variable selection using Stan+R
- Sampling Polya-Gamma random variates: alternate and approximate techniques
- Shrinkage regression for multivariate inference with missing data, and an application to portfolio balancing
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- Bayesian Semiparametric Modeling of Response Mechanism for Nonignorable Missing Data
- Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net