equalCovs: LC-test for equality of high dimensional covariances
In meca7653/HDtest-2018-8: High Dimensional Hypothesis Testing for Mean Vectors, Covariance Matrices, and White Noise of Vector Time Series

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

1	equalCovs(X, Y, alpha, DNAME)

`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.

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

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

Tong He

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

meca7653/HDtest-2018-8 documentation built on May 16, 2019, 3:13 p.m.

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