Bayesian Penalized Quantile Regression
Bayesian regularized quantile regression utilizing sparse priors to impose exact sparsity leads to efficient Bayesian shrinkage estimation, variable selection and statistical inference. In this package, we have implemented robust Bayesian variable selection with spike-and-slab priors under high-dimensional linear regression models (Fan et al. (2024) and Ren et al. (2023), and regularized quantile varying coefficient models (Zhou et al.(2023)). In particular, valid robust Bayesian inferences under both models in the presence of heavy-tailed errors can be validated on finite samples. Additional models including robust Bayesian group LASSO and robust Bayesian binary LASSO are also included. The Markov Chain Monte Carlo (MCMC) algorithms of the proposed and alternative models are implemented in C++.
install.packages("devtools")
devtools::install_github("cenwu/pqrBayes")install.packages("pqrBayes")Data <- function(n,p,quant){
sig1 = matrix(0,p,p)
diag(sig1)=1
for (i in 1: p)
{
for (j in 1: p)
{
sig1[i,j]=0.5^abs(i-j)
}
}
xx = MASS::mvrnorm(n,rep(0,p),sig1)
x = cbind(1,xx)
error=rt(n,2) -quantile(rt(n,2),probs = quant) # can also be changed to normal error for non-robust setting
beta = c(0,1,1.5,2,rep(0,p-3))
betaa = beta[-1]
y = x%*%beta+error
dat = list(y=y, x=xx, beta=betaa)
return(dat)
}n=100; p=500; rep=1000;
quant = 0.5; # focus on median for Bayesian inference
CI_RBLSS = CI_RBL = CI_BLSS = CI_BL= matrix(0,rep,p)
for (h in 1:rep) {
dat = Data(n,p,quant)
y = dat$y
g = dat$x
coefficient = dat$beta
# an intercept is automatically included by the package
fit = pqrBayes(g, y, u=NULL, d=NULL,e=NULL,quant=quant, iterations=10000, burn.in = NULL, spline = NULL,robust = TRUE, sparse=TRUE, model = "linear", hyper=NULL,debugging=FALSE)
coverage = coverage(fit,coefficient,u.grid=NULL, model = "linear")
fit1 = pqrBayes(g, y, u=NULL,d=NULL, e=NULL,quant=quant, iterations=10000, burn.in = NULL, spline = NULL, robust = TRUE, sparse=FALSE, model = "linear", hyper=NULL,debugging=FALSE)
coverage1 = coverage(fit1,coefficient,u.grid=NULL, model = "linear")
fit2 = pqrBayes(g, y, u=NULL,d=NULL, e=NULL,quant=quant, iterations=10000, burn.in = NULL, spline = NULL, robust = FALSE, sparse=TRUE, model= "linear", hyper=NULL,debugging=FALSE)
coverage2 = coverage(fit2,coefficient,u.grid=NULL, model = "linear")
fit3 = pqrBayes(g, y, u=NULL,d=NULL, e=NULL,quant=quant, iterations=10000, burn.in = NULL, spline = NULL, robust = FALSE, sparse=FALSE, model = "linear", hyper=NULL,debugging=FALSE)
coverage3 = coverage(fit3,coefficient,u.grid=NULL, model = "linear")
CI_RBLSS[h,] = coverage
CI_RBL[h,] = coverage1
CI_BLSS[h,] = coverage2
CI_BL[h,] = coverage3
cat("Replicate = ", h, "\n")
}
# the intercept has not been regularized
cp_RBLSS = colMeans(CI_RBLSS)[1:3] # 95% empirical coverage probabilities for coefficients under the robust linear model
cp_BLSS = colMeans(CI_BLSS)[1:3]
cp_RBL = colMeans(CI_RBL)[1:3]
cp_BL = colMeans(CI_BL)[1:3]Data <- function(n,p,quant){
sig1 = matrix(0,p,p)
diag(sig1)=1
for (i in 1: p)
{
for (j in 1: p)
{
sig1[i,j]=0.5^abs(i-j)
}
}
x = MASS::mvrnorm(n,rep(0,p),sig1)
x = cbind(1,x)
error=rt(n,2) -quantile(rt(n,2),probs = quant)
u = runif(n,0.01,0.99)
gamma0 = 2+2*sin(u*2*pi)
gamma2 = -6*u*(1-u)
gamma1 = 2*exp(2*u-1)
gamma3= -4*u^3
y = gamma1*x[,2] + gamma2*x[,3] + gamma3*x[,4] + gamma0 + error
dat = list(y=y, u=u, x=x, gamma=cbind(gamma0,gamma1,gamma2,gamma3))
return(dat)
}n=250; p=100; # the actual dimension after basis expansion is 505
rep=200;
quant = 0.5; # focus on median for Bayesian inference
CI_RBGLSS = CI_RBGL = CI_BGLSS = CI_BGL= c()
for (h in 1:rep) {
dat = Data(n,p,quant)
y = dat$y
u = dat$u
x = dat$x
g = x[,-1]
kn=2
degree=2
u.grid = (1:200)*0.005
gamma_0_grid = 2+2*sin(2*u.grid*pi)
gamma_1_grid = 2*exp(2*u.grid-1)
gamma_2_grid = -6*u.grid*(1-u.grid)
gamma_3_grid = -4*u.grid^3
coefficient = cbind(gamma_0_grid,gamma_1_grid,gamma_2_grid,gamma_3_grid)
# a varying intercept is automatically included by the package
fit = pqrBayes(g, y, u, d=NULL,e=NULL,quant=quant, iterations=10000, burn.in = NULL, spline = list(kn=2,degree=2), robust = TRUE, sparse=TRUE, model = "VC", hyper=NULL,debugging=FALSE)
coverage = coverage(fit,coefficient,u.grid, model = "VC")
fit1 = pqrBayes(g, y, u, d=NULL,e=NULL,quant=quant, iterations=10000, burn.in = NULL, spline = list(kn=2, degree=2), robust = TRUE, sparse=FALSE, model = "VC", hyper=NULL,debugging=FALSE)
coverage1 = coverage(fit1,coefficient,u.grid, model = "VC")
fit2 = pqrBayes(g, y, u, d = NULL,e=NULL,quant=quant, iterations=10000, burn.in = NULL, spline = list(kn=2, degree=2), robust = FALSE, sparse=TRUE, model = "VC", hyper=NULL,debugging=FALSE)
coverage2 = coverage(fit2,coefficient,u.grid, model = "VC")
fit3 = pqrBayes(g, y, u, d=NULL,e=NULL,quant=quant, iterations=10000, burn.in = NULL, spline = list(kn=2, degree=2), robust = FALSE, sparse=FALSE, model = "VC", hyper=NULL,debugging=FALSE)
coverage3 = coverage(fit3,coefficient,u.grid,model = "VC")
CI_RBGLSS = rbind(CI_RBGLSS,coverage)
CI_RBGL = rbind(CI_RBGL,coverage1)
CI_BGLSS = rbind(CI_BGLSS,coverage2)
CI_BGL = rbind(CI_BGL,coverage3)
cat("Replicate = ", h, "\n")
}
# the varying intercept has not been regularized
cp_RBGLSS = colMeans(CI_RBGLSS) # 95% empirical coverage probabilities for the varying coefficients under the default setting
cp_BGLSS = colMeans(CI_BGLSS)
cp_RBGL = colMeans(CI_RBGL)
cp_BGL = colMeans(CI_BGL)Data <- function(n,p,quant){
sig1 = matrix(0,p,p)
diag(sig1)=1
for (i in 1: p)
{
for (j in 1: p)
{
sig1[i,j]=0.5^abs(i-j)
}
}
xx = MASS::mvrnorm(n,rep(0,p),sig1)
x = cbind(1,xx)
error=rt(n,2) -quantile(rt(n,2),probs = quant) # can also be changed to normal error for non-robust setting
beta = c(0,1,1.5,2,0,0,0,0.5,0.55,0.6,rep(0,p-9))
betaa = beta[-1]
y = x%*%beta+error
dat = list(y=y, x=xx, beta=betaa)
return(dat)
}n=100; p=300;
quant = 0.5; # focus on median for Bayesian estimation
dat = Data(n,p,quant)
y = dat$y
g = dat$x
coefficient = dat$beta
# an intercept is automatically included by the package
# the intercept has not been regularized
fit = pqrBayes(g, y, u=NULL,d=3, e=NULL,quant=quant, iterations=10000, burn.in = NULL, spline = NULL,robust = TRUE, sparse=TRUE, model = "group", hyper=NULL,debugging=FALSE)
estimation_1 = estimation.pqrBayes(fit,coefficient,model="group")
coeff_est_1 = estimation_1$coeff.est
mse_1 = estimation_1$error$MSE
fit1 = pqrBayes(g, y, u=NULL,d=3, e=NULL,quant=quant, iterations=10000, burn.in = NULL, spline = NULL, robust = TRUE, sparse=FALSE, model = "group", hyper=NULL,debugging=FALSE)
estimation_2 = estimation.pqrBayes(fit1,coefficient,model="group")
coeff_est_2 = estimation_2$coeff.est
mse_2 = estimation_2$error$MSE
fit2 = pqrBayes(g, y, u=NULL,d=3, e=NULL,quant=quant, iterations=10000, burn.in = NULL, spline = NULL, robust = FALSE, sparse=TRUE, model= "group", hyper=NULL,debugging=FALSE)
estimation_3 = estimation.pqrBayes(fit2,coefficient,model="group")
coeff_est_3 = estimation_3$coeff.est
mse_3 = estimation_3$error$MSE
fit3 = pqrBayes(g, y, u=NULL,d=3, e=NULL,quant=quant, iterations=10000, burn.in = NULL, spline = NULL, robust = FALSE, sparse=FALSE, model = "group", hyper=NULL,debugging=FALSE)
estimation_4 = estimation.pqrBayes(fit3,coefficient,model="group")
coeff_est_4 = estimation_4$coeff.est
mse_4 = estimation_4$error$MSE This package provides implementation for methods from