Description Usage Arguments Value Examples
Efficiency first conformal prediction for ridge regression
1 |
X |
A N*d training matrix |
Y |
A N*1 training vector |
X0 |
A N0*d testing vector |
lambda |
a sequence of penalty parameters for ridge regression |
alpha |
miscoverage level |
upper and lower prediction intervals for X0.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | df=3
d = 5
n=50 #number of training samples
n0=10 #number of prediction points
rho=0.5
Sigma=matrix(rho,d,d)
diag(Sigma)=rep(1,d)
beta=rep(1:5,d/5)
X0=mvtnorm::rmvt(n0,Sigma,df)
X=mvtnorm::rmvt(n,Sigma,df) #multivariate t distribution
eps=rt(n,df)*(1+sqrt(X[,1]^2+X[,2]^2))
Y=X%*%beta+eps
out.efcp=efcp.fun(X,Y,X0)
out.efcp$up
out.efcp$lo
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