A Bayesian Binary reciprocal LASSO quantile regression (with practical application)


  • Mohammed Kahnger University of Kufa
  • Ahmad Naeem Flaih University of Al-Qadisiyah




Quantile regression , variable selection, binary quantile regression.


Quantile regression is one of the methods that has taken a wide space in application in the previous two decades because of the attractive features of these methods to researchers, as it is not affected by outliers values, meaning that it is considered one of the robust methods, and it gives more details of the effect of explanatory variables on the dependent variable.In this paper, a Bayesian hierarchical model for variable selection and estimation in the context of binary quantile regression is proposed. Current approaches to variable selection in the context of binary classification are sensitive to outliers, heterogeneous values, and other anomalies. The proposed method in this study overcomes these problems in an attractive and direct way.


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Benoit DF, Van den Poel D (2012) Binary Quantile Regression: A Bayesian Approach Based on the Asymmetric Laplace Distribution. J App Econom 27:1174{1188. DOI: https://doi.org/10.1002/jae.1216

Efron, B., T. Hastie, I. Johnstone, R. Tibshirani, et al. (2004). Least angle regression. The Annals of statistics 32 (2), 407–499. DOI: https://doi.org/10.1214/009053604000000067

Fan, J. and R. Li (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American statistical Association 96 (456), 1348–1360. DOI: https://doi.org/10.1198/016214501753382273

Koenker, R., Bassett G. J., 1978, “Regression quantiles”, Econometrica, 46, pp.33-50. DOI: https://doi.org/10.2307/1913643

Keming Yu& Rana A.Moyeed (2001)" Bayesian Quantile Regression" Statistical & Probability Letters 54:437-447. DOI: https://doi.org/10.1016/S0167-7152(01)00124-9

Keming Yu, Zudi Lu and Julian Stander (2003) “Quantile regression :applications and current research areas” The Statistician 52, Part 3, pp. 331-350. DOI: https://doi.org/10.1111/1467-9884.00363

Kordas G (2006) Smoothed Binary Regression Quantiles. J Appl Econom 21:387- 407. DOI: https://doi.org/10.1002/jae.843

Li Q, Xi R, Lin N (2010) Bayesian Regularized Quantile Regression. Bayesian Analysis 5:1-24. DOI: https://doi.org/10.1214/10-BA506

Mallick, H., Yi, N., 2014, “A new Bayesian lasso”, Statistics and its interface, 7: pp. 571-582. DOI: https://doi.org/10.4310/SII.2014.v7.n4.a12

Meinshausen, N. (2007). Relaxed lasso. Computational Statistics & Data Analysis 52 (1), 374–393. DOI: https://doi.org/10.1016/j.csda.2006.12.019

Mallick, H., Alhamzawi, R., Paul, E., & Svetnik, V. (2021). The reciprocal Bayesian lasso. Statistics in Medicine, 40(22), 4830-4849. DOI: https://doi.org/10.1002/sim.9098

Qifan Song and Faming Liang. High-dimensional variable selection with reciprocal 1-Regularization.Jornal of the American Statistical Association, 110(512):1607-1620,2015. DOI: https://doi.org/10.1080/01621459.2014.984812

Radchenko, P. and G. M. James (2008). Variable inclusion and shrinkage algorithms. Journal of the American Statistical Association 103 (483), 1304–1315. DOI: https://doi.org/10.1198/016214508000000481

Tibshirani (1996)"Regression Shrinkage and selection via lasso" J. R.Statis.Soc.58,No.1,pp. 267-288. DOI: https://doi.org/10.1111/j.2517-6161.1996.tb02080.x




How to Cite

Kahnger, M. ., & Flaih, A. N. (2023). A Bayesian Binary reciprocal LASSO quantile regression (with practical application) . Journal of Kufa for Mathematics and Computer, 10(1), 13–17. https://doi.org/10.31642/JoKMC/2018/100102

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