Sparsity in Bayesian Elastic Net in Tobit Regression with an
Application
Ahmad Naeem Flaih
Mohammed Rasool ALsafi
Al-Qadisiyah University – College of Administration and Economics
Corresponding Author : Mohammed Rasool ALsafi
Abstract : In this paper we developed one of the most well-known regularization methods that is called elastic net
method in tobit regression from the Bayesian point of views. This regularization adding the ridge and lasso penalty
functions to the residual sum of squares term. In this paper, we developed new Bayesian hierarchical model for the
tobit regression based on the proposed scale mixture of Li and Lin, (2010) that mixing the normal distribution with
truncated gamma distribution (1,∞) as double exponential prior distribution for the interested parameter (β).
Furthermore, the MCMC Gibbs sampling algorithm has developed for the posterior distribution of intereste
