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 method is "subsampling")

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.


[Package QuadratiK version 1.1.1 Index]