Description Usage Arguments Value References See Also
The two-sample test for high-dimensional covariance matrices in Chang, Zhou, Zhou, and Wang (2016) "Comparing Large Covariance Matrices under Weak Conditions on the Dependence Structure". Using a wild boostrap procedure, the test statistic is essentially the square root of the statistic proposed in Cai, Liu and Xia (2013).
1 | Chang.maxBoot.test(X, Y, nresample = 1000, useMC = TRUE, mc.cores = 1)
|
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 |
nresample |
the number of bootstraps to perform |
useMC |
logical variable indicating whether to use multicore parallelization.
R packages |
mc.cores |
decide the number of cores to use when |
A list with the following components:
test.stat |
test statistic |
test.stat.boot |
bootstrap test statistics, a numeric vector with length nresample |
pVal |
bootstrap p-value |
Chang, Zhou, Zhou, and Wang (2016) "Comparing Large Covariance Matrices under Weak Conditions on the Dependence Structure", arXiv preprint arXiv:1505.04493.
Cai.max.test()
, LC.U.test()
, WL.randProj.test()
, Schott.Frob.test()
.
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