jpen.inv: JPEN estimate of inverse cov matrix
In JPEN: Covariance and Inverse Covariance Matrix Estimation Using Joint Penalty

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/JPEN.R

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

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

`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.

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.

Returns a well conditioned and positive inverse covariance matrix.

Ashwini Maurya, Email: mauryaas@msu.edu.

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

jpen,jpen.tune,jpen.inv.tune

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);