Qrbl: Bayesian Lasso Binary quantile regression

In Brq: Bayesian Analysis of Quantile Regression Models

Description

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.

Usage

BLBqr(x,y, tau = 0.5, runs = 11000, burn = 1000, thin=1)

Arguments

x	Matrix of predictors.
y	Vector of dependent variable.
tau	The quantile of interest. Must be between 0 and 1.
runs	Length of desired Gibbs sampler output.
burn	Number of Gibbs sampler iterations before output is saved.
thin	thinning parameter of MCMC draws.

Author(s)

Rahim Alhamzawi

Brq documentation built on July 1, 2020, 7:07 p.m.