Cai.max.test: Two-sample covariance test (Cai et al. 2013)

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_\infinity norm, proposed in Cai, Liu and Xia (2013) "Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings".

Usage

Cai.max.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 with the following components:

Mn	the largest M_ij as defined in Cai (2013) equation (2)
test.stat	test statistic (calculated as Mn - 4*log p + log log p)
pVal	p-value given by the limiting distribution (Gumbol distribution)

References

Cai, Liu and Xia (2013) "Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings", Journal of the American Statistical Association.

See Also

Chang.maxBoot.test(), LC.U.test(), WL.randProj.test(), Schott.Frob.test().

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