compute_CV {QuadratiK} | R Documentation |
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)
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 |
size_y |
the number of rows in the |
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 |
Value
the critical value for the specified method and significance level.
References
Markatou Marianthi, Saraceno Giovanni, Chen Yang (2023). “Two- and k-Sample Tests Based on Quadratic Distances.” Manuscript, (Department of Biostatistics, University at Buffalo)