Bayesian Binary reciprocal LASSO quantile regression (with practical application)

Abstract

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.