cv_ksample {QuadratiK} | R Documentation |
Compute the critical value for the KBQD k-sample tests
Description
This function computes the empirical critical value for the k-sample KBQD tests using the centered Gaussian kernel, with bootstrap, permutation, or subsampling.
Usage
cv_ksample(x, y, h, B = 150, b = 0.9, Quantile = 0.95, method = "subsampling")
Arguments
x |
matrix containing the observations to be used in the k-sample test |
y |
vector indicating the sample for each observation |
h |
the tuning parameter for the test using the Gaussian kernel |
B |
the number of bootstrap/permutation/subsampling samples to generate |
b |
the subsampling block size (only used if |
Quantile |
the quantile of the bootstrap/permutation/subsampling distribution to use as the critical value |
method |
the method to use for computing the critical value (one of "bootstrap", "permutation") |
Value
a vector of two critical values corresponding to different formulation of the k-sample test statistics.