New Bayesian Lasso Composite Quantile Regression
In this paper, we propose a new Bayesian lasso inference scheme for variable selection in composite quantile regression model (C Quantile Reg). The suggested approach is to construct a hierarchical structure within the Gibbs sampling under the assumption that the residual term comes from skew Laplace distribution (asymmetric Laplace distribution) and assign scale mixture uniform (SMU) as prior distributions on the coefficients of composite quantile regression model. Our proposed method was compared to some other existing methods by testing the performance of these methods through simulation studies and real data examples.
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