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The Bayesian elastic net regression
Authors:Rahim Alhamzawi  Haithem Taha Mohammad Ali
Institution:1. Department of Statistics, College of Administration and Economics, University of Al-Qadisiyah, Iraq;2. College of Computers and Information Technology, Nawroz University, Duhok, Iraq
Abstract:A Bayesian elastic net approach is presented for variable selection and coefficient estimation in linear regression models. A simple Gibbs sampling algorithm was developed for posterior inference using a location-scale mixture representation of the Bayesian elastic net prior for the regression coefficients. The penalty parameters are chosen through an empirical method that maximizes the data marginal likelihood. Both simulated and real data examples show that the proposed method performs well in comparison to the other approaches.
Keywords:Bayesian inference  Elastic net  Lasso  MCMC  Prior distribution
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