Description Usage Arguments Value References See Also

View source: R/simulation-CZZW.R

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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