jpen.inv {JPEN}

JPEN estimate of inverse cov matrix

Description

A well conditioned and sparse estimate of inverse covariance matrix using Joint Penalty

Usage

jpen.inv(S, gam, lam=NULL)

Arguments

S

Sample cov matrix or a positive definite estimate based on covariance matrix.

gam

gam is tuning parameter for eigenvalues shrinkage.

lam

lam is tuning parameter for sparsity.

Details

Estimates a well conditioned and sparse inverse covariance matrix using Joint Penalty. If input matrix is singular or nearly singular, a JPEN estimate of covariance matrix is used in place of S.

Value

Returns a well conditioned and positive inverse covariance matrix.

Author(s)

Ashwini Maurya, Email: [e-mail].

References

A Well Conditioned and Sparse Estimate of Covariance and Inverse Covariance Matrix Using Joint Penalty. Submitted. http://arxiv.org/pdf/1412.7907v2.pdf

See Also

jpen,jpen.tune,jpen.inv.tune

Examples

p=10;n=100;
Sig=diag(p);
y=rmvnorm(n,mean=rep(0,p),sigma=Sig);
S=var(y);
gam=1.0;
lam=2*max(abs(S[col(S)!=row(S)]))/p;
Omghat=jpen.inv(var(y),gam,lam);