Chang.wildBootstrap: Wild bootstrap

In lingxuez/sLED: A Sparse Leading Eigenvalue Driven (sLED) Test for High-dimensional Matrices

Description

Perform the wild boostrap procedure for testing two-sample covariance matrices in Chang et al. (2016).

Usage

Chang.wildBootstrap(X, Y, sigma.x, sigma.y, Tdenominator, nresample = 1000,
  useMC = TRUE, mc.cores = 1)

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
sigma.x	p-by-p matrix, sample covariance of X, pre-calculated
sigma.y	p-by-p matrix, sample covariance of X, pre-calculated
Tdenominator	the denominator of the test statistics, which is pre-calculated and remains unchanged in bootstrap.
nresample	the number of bootstraps to perform
useMC	logical variable indicating whether to use multicore parallelization. R packages parallel and doParallel are required if set to TRUE.
mc.cores	decide the number of cores to use when useMC is set to TRUE.

Value

A numeric vector with length nresample, containing the test statistics in wild boostrap repetitions.

See Also

Chang.maxBoot.test().

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