all_data=c()
for(k in 1:50)
{
p=8
n=200
tbeta=c(3,1.5,2,rep(0,p-3))
Sig=matrix(rep(1,p*p),p)
for (ii in 1:p) for (jj in 1:p) if (ii!=jj) Sig[ii,jj] = 0.5^(abs(ii-jj))
x = mvrnorm(n, rep(0,p), Sig)
y = x%*%tbeta + rnorm(n,0,3)
cv.out<-cv.glmnet(x,y,nfolds=5)
tbetahat<-coef(cv.out)[2:9]
yhat = x%*%tbetahat + rnorm(n,0,3)
a = y-yhat
pse=sum(a^2)/n
all_data=c(all_data,pes)