equalCovs: LC-test for equality of high dimensional covariances

In HDtest: High Dimensional Hypothesis Testing for Mean Vectors, Covariance Matrices, and White Noise of Vector Time Series

Description

Testing the equality of two high dimensional covariance matrices using the testing procedure by Li and Chen (2012).

Usage

equalCovs(X, Y, alpha, DNAME)

Arguments

X	The n x p data matrix from the sample 1
Y	The n x p data matrix from the sample 2.
alpha	The prescribed level of significance
DNAME	Default input.

Details

Implementing testing procedure proposed by Li and Chen (2012) to test the equality of two sample high dimensional covariance matrices.

Value

Value of testing statistic, p-value, alternative hypothesis, and the name of testing procedure.

Author(s)

Tong He

References

J. Li and S. Chen (2012). Two sample tests for high-dimensional covariance matrices. Ann. Statist. 40, 908–940

HDtest documentation built on May 2, 2019, 11:53 a.m.