BLtqr {Brq}  R Documentation 
Bayesian tobit quantile regression
Description
This function implements the idea of Bayesian Lasso tobit 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 as in Reed and Yu (2009). This function implements the Bayesian lasso for linear tobit quantile regression models by assigning scale mixture of normal (SMN) priors on the parameters and independent exponential priors on their variances. A Gibbs sampling algorithm for the Bayesian Lasso tobit quantile regression is constructed by sampling the parameters from their full conditional distributions.
Usage
BLtqr(x,y, tau = 0.5, left = 0, runs = 11000, burn = 1000, thin=1)
Arguments
x 

y 

tau 

left 

runs 

burn 

thin 

Author(s)
Rahim Alhamzawi
Examples
# Example
n < 150
p=8
Beta=c(5, 0, 0, 0, 0, 0, 0, 0)
x < matrix(rnorm(n=p*n),n)
x=scale(x)
y <x%*%Beta+rnorm(n)
y=ymean(y)
y=pmax(0, y)
fit = Brq(y~0+x,tau=0.5, method="BLtqr",runs=5000, burn=1000)
summary(fit)
model(fit)