This function implements the idea of Bayesian Lasso Binary quantile regression using a likelihood function that is based on the asymmetric Laplace distribution (Rahim, 2016). The asymmetric Laplace error distribution is written as a scale mixture of normal distributions.
BLBqr(x,y, tau = 0.5, runs = 11000, burn = 1000, thin=1)
Matrix of predictors.
Vector of dependent variable.
The quantile of interest. Must be between 0 and 1.
Length of desired Gibbs sampler output.
Number of Gibbs sampler iterations before output is saved.
thinning parameter of MCMC draws.