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Brq: an R package for Bayesian quantile regression

Author

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  • Rahim Alhamzawi

    (University of Al-Qadisiyah)

  • Haithem Taha Mohammad Ali

    (Nawroz University)

Abstract

Bayesian regression quantile has received much attention in recent literature. The objective of this paper is to illustrate Brq, a new software package in R. Brq allows for the Bayesian coefficient estimation and variable selection in regression quantile (RQ) and support Tobit and binary RQ. In addition, this package implements the Bayesian Tobit and binary RQ with lasso and adaptive lasso penalties. Further modeling functions for summarising the results, drawing trace plots, posterior histograms, autocorrelation plots, and plotting quantiles are included.

Suggested Citation

  • Rahim Alhamzawi & Haithem Taha Mohammad Ali, 2020. "Brq: an R package for Bayesian quantile regression," METRON, Springer;Sapienza Università di Roma, vol. 78(3), pages 313-328, December.
    • Handle: RePEc:spr:metron:v:78:y:2020:i:3:d:10.1007_s40300-020-00190-6
    DOI: 10.1007/s40300-020-00190-6
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    References listed on IDEAS

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    1. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    2. Geraci, Marco, 2014. "Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i13).
    3. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
    4. Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
    5. Maria Marino & Marco Alfó, 2015. "Latent drop-out based transitions in linear quantile hidden Markov models for longitudinal responses with attrition," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 483-502, December.
    6. Yue, Yu Ryan & Rue, Håvard, 2011. "Bayesian inference for additive mixed quantile regression models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 84-96, January.
    7. Yu, Keming & Stander, Julian, 2007. "Bayesian analysis of a Tobit quantile regression model," Journal of Econometrics, Elsevier, vol. 137(1), pages 260-276, March.
    8. R. Alhamzawi & K. Yu & D. F. Benoit, 2011. "Bayesian adaptive Lasso quantile regression," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/728, Ghent University, Faculty of Economics and Business Administration.
    9. Dries Benoit & Rahim Alhamzawi & Keming Yu, 2013. "Bayesian lasso binary quantile regression," Computational Statistics, Springer, vol. 28(6), pages 2861-2873, December.
    10. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    11. Stephen Portnoy & Guixian Lin, 2010. "Asymptotics for censored regression quantiles," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(1), pages 115-130.
    12. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    13. Dries F. Benoit & Dirk Van den Poel, 2012. "Binary quantile regression: a Bayesian approach based on the asymmetric Laplace distribution," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1174-1188, November.
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    2. Magzhanov, Timur & Sagradyan, Anna, 2023. "Ambiguous high scores: The All-Russian Olympiad in economics during the COVID-19 pandemic," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 89-108.
    3. Mai Dao & Min Wang & Souparno Ghosh & Keying Ye, 2022. "Bayesian variable selection and estimation in quantile regression using a quantile-specific prior," Computational Statistics, Springer, vol. 37(3), pages 1339-1368, July.

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