cernn (version 0.1)

shrink_eigen: Nonlinear shrinkage of sample eigenvalues

Description

shrink_eigen shrinks the sample eigenvalues.

Usage

shrink_eigen(d, lambda, alpha, n)

Arguments

d

Vector of sample eigenvalues to shrink. These must be nonnegative.

lambda

Regularization parameter controling amount of shrinkage towards the target.

alpha

Parameter that controls mixture between the trace and inverse trace penalties.

n

The number of observations.

Value

Vector of shrunken eigenvalues.

Examples

set.seed(12345)
nLambda <- 100
lambda <- 10**seq(-2,2,length.out=nLambda)
alpha <- 0.5
n <- 10
p <- 5
d <- sort(2*runif(p))
e <- shrink_eigen(d,lambda,alpha,n)

## Plot regularization paths of eigenvalues
matplot(x=log10(lambda),y=t(e),type='l',ylab='shrunken eigenvalue')