compute_CV: Compute the critical value for two-sample KBQD tests

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

Compute the critical value for two-sample KBQD tests

Description

This function computes the critical value for two-sample kernel tests with centered Gaussian kernel using one of three methods: bootstrap, permutation, or subsampling.

Usage

compute_CV(
  B,
  Quantile,
  data_pool,
  size_x,
  size_y,
  h,
  method,
  b = 1,
  compute_variance
)

Arguments

B	the number of bootstrap/permutation/subsampling samples to generate.
Quantile	the quantile of the bootstrap/permutation/subsampling distribution to use as the critical value.
data_pool	a matrix containing the data to be used in the test.
size_x	the number of rows in the data_pool matrix corresponding to group X.
size_y	the number of rows in the data_pool matrix corresponding to group Y.
h	the tuning parameter for the kernel test.
method	the method to use for computing the critical value (one of "bootstrap", "permutation", or "subsampling").
b	the subsampling block size (only used if method is "subsampling").
compute_variance	indicates if the nonparametric variance is computed. Default is TRUE.

Value

the critical value for the specified method and significance level.

References

Markatou Marianthi & Saraceno Giovanni (2024). “A Unified Framework for Multivariate Two- and k-Sample Kernel-based Quadratic Distance Goodness-of-Fit Tests.” https://doi.org/10.48550/arXiv.2407.16374

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