cv_ksample: Compute the critical value for the KBQD k-sample tests

In QuadratiK: Collection of Methods Constructed using Kernel-Based Quadratic Distances

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",
  compute_variance = TRUE
)

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")
compute_variance	indicates if the nonparametric variance is computed. Default is TRUE.

Value

a vector of two critical values corresponding to different formulation of the k-sample test statistics.

QuadratiK documentation built on Oct. 29, 2024, 5:08 p.m.