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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Fadel Hamid Hadi Alhusseini & Taha al Shaybawee & Fedaa Abd Almajid Sabbar Alaraje, 2017. "Identify Relative importance of covariates in Bayesian lasso quantile regression via new algorithm in statistical program R," Romanian Statistical Review, Romanian Statistical Review, vol. 65(4), pages 99-110, December.
    • Handle: RePEc:rsr:journl:v:65:y:2017:i:4:p:99-110
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Bayesian lasso quantile regression; Prior distributions; posterior distributions; MCMC algorithm; Relative importance;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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