Identify Relative importance of covariates in Bayesian lasso quantile regression via new algorithm in statistical program R

Identify Relative importance of covariates in Bayesian lasso quantile regression via new algorithm in statistical program R

In this paper, we propose a new algorithm to determine the relative importance of covariates by Bayesian Lasso quantile regression for variable selection assigning new formula of Laplace distributions for the regression parameters. Simple and efficient Markov chain Monte Carlo (M.C.M.C) algorithm wa...

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Bibliographic Details
Main Authors: Fadel Hamid Hadi Alhusseini, Taha al Shaybawee, Fedaa Abd Almajid Sabbar Alaraje
Format: Article
Language:English
Published: Romanian National Institute of Statistics 2017-11-01
Series:Revista Română de Statistică
Subjects:
Bayesian lasso quantile regression
Prior distributions
posterior distributions
MCMC algorithm
Relative importance
Online Access:http://www.revistadestatistica.ro/wp-content/uploads/2017/11/RRS-4_2017_tipar_A08.pdf

Internet

http://www.revistadestatistica.ro/wp-content/uploads/2017/11/RRS-4_2017_tipar_A08.pdf

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