Description Usage Arguments Author(s) Examples
This function implements the idea of Bayesian tobit quantile regression employing a likelihood function that is based on the asymmetric Laplace distribution (Yu and Stander, 2007). The asymmetric Laplace error distribution is written as scale mixtures of normal distributions as in Reed and Yu (2009).
1  Btqr(x,y, tau = 0.5, left = 0, runs = 11000, burn = 1000, thin=1)

x 

y 

tau 

left 

runs 

burn 

thin 

Rahim Alhamzawi
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  # Example
set.seed(12345)
x < abs(rnorm(100))
y < 0.5 + x +(.25 + .25*x)*rnorm(100)
plot(x,y, type="n")
h <(y > 0)
points(x[h],y[h],cex=.9,pch=16)
points(x[!h],y[!h],cex=.9,pch=1)
y < pmax(0,y)
for(tau in (2:8)/9){
fit=Brq(y~x,tau=tau, method="Btqr", left=0, runs=1000, burn=500)$coef
# Note: runs =11000 and burn =1000
Xs=sort(x)
Xc=cbind(1,sort(x))
Xcf=Xc%*%c(fit)
Xcfp=pmax(0,Xcf)
lines(Xs,Xcfp,col="red")}

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