jpen.inv.tune: Tuning parameter Selection for inverse covariance matrix...

In JPEN: Covariance and Inverse Covariance Matrix Estimation Using Joint Penalty

Description

Returns optimal values of tuning parameters lambda and gamma

Usage

jpen.inv.tune(Ytr, gama, lambda=NULL)

Arguments

Ytr	Ytr is matrix of observations.
gama	A vector of gamma values.
lambda	Optional vector of values of lambda. If optional, the algorithm automatically calculates 10 values of lambda for each gamma and finds the optimal values of (lambda,gamma) that minimizes the negative of Gaussian likelihood function using K-fold cross validation.

Details

Returns the value of optimal tuning parameters. The function uses K-fold cross validation to select the best tuning parameter from among a set of of values of lambda and gamma.

Value

Returns the optimal values of lambda and gamma.

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

Examples

p=10;n=100;
Sig=diag(p);
y=rmvnorm(n,mean=rep(0,p),sigma=Sig);
gama=c(0.5,1.0);
opt=jpen.inv.tune(var(y),gama);

JPEN documentation built on May 2, 2019, 5:54 a.m.