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

Fadel Hamid Hadi Alhusseini, Taha al Shaybawee and Fedaa Abd Almajid Sabbar Alaraje
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Fadel Hamid Hadi Alhusseini: Department of Statistics and Informatics, University of Craiova, Romania
Taha al Shaybawee: Faculty of Economics and Business Administration, Al-Qadiseya University, Iraq
Fedaa Abd Almajid Sabbar Alaraje: Department of Accounting, University of Craiova, Romania

Romanian Statistical Review, 2017, vol. 65, issue 4, 99-110

Abstract: 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 was introduced for Bayesian sampler. Simulation approaches and two real data set are used to assess the performance of the proposed method. Both simulated and real data sets show that the performs of the proposed method is quite good for Identify Relative importance of covariates.

Keywords: Bayesian lasso quantile regression; Prior distributions; posterior distributions; MCMC algorithm; Relative importance (search for similar items in EconPapers)
JEL-codes: C11 C21 C52 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:rsr:journl:v:65:y:2017:i:4:p:99-110

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