Schott.Frob.test: Two-sample covariance test (Schott 2007)

In lingxuez/sLED: A Sparse Leading Eigenvalue Driven (sLED) Test for High-dimensional Matrices

Description

The two-sample covariance test proposed in Schott (2007) "A test for the equality of covariance matrices when the dimension is large relative to the sample sizes". The original test procedure handles multiple multivariate Normal distributions, but for the purpose of our simulation, the implementation is for two populations only.

Usage

Schott.Frob.test(X, Y)

Arguments

X	n1 by p matrix, observation of the first population, columns are features
Y	n2 by p matrix, observation of the second population, columns are features

Value

A list containing the following components:

test.stat	test statistic
pVal	the p-value calculated using the limiting distribution (standrad normal)

References

Schott (2007) "A test for the equality of covariance matrices when the dimension is large relative to the sample sizes", Computational Statistics & Data Analysis.

See Also

Cai.max.test(), Chang.maxBoot.test(), LC.U.test(), WL.randProj.test().

lingxuez/sLED documentation built on May 7, 2019, 2:55 a.m.