LC.U.test: Two-sample covariance test (Li and Chen 2012)

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

Description

Testing the equality of two high-dimensional covariance matrices based on the L_2 norm, proposed in Li and Chen (2012) "Two Sample Tests for High-dimensional Covariance Matrices"

Usage

LC.U.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:

Tn	the U statistic for ||cov(X)-cov(Y)||_F^2
Tn.sd	the estimated standard deviation of Tn
test.stat	test statistic, Tn/Tn.sd
pVal	the p-value calculated using the limiting distribution (standard normal)

References

Li and Chen (2012) "Two Sample Tests for High-dimensional Covariance Matrices", The Annals of Statistics.

See Also

Cai.max.test(), Chang.maxBoot.test(), WL.randProj.test(), Schott.Frob.test().

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