normal_CV: Compute the critical value for the KBQD tests for...

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

Compute the critical value for the KBQD tests for multivariate Normality

Description

This function computes the empirical critical value for the Normality test based on the KBQD tests using the centered Gaussian kernel.

Usage

normal_CV(d, size, h, mu_hat, Sigma_hat, B = 150, Quantile = 0.95)

Arguments

d	the dimension of generated samples.
size	the number of observations to be generated.
h	the concentration parameter for the Gaussian kernel.
mu_hat	Mean vector for the reference distribution.
Sigma_hat	Covariance matrix of the reference distribution.
B	the number of replications.
Quantile	the quantile of the distribution use to select the critical value

Details

For each replication, a sample from the d-dimensional Normal distribution with mean vector mu_hat and covariance matrix Sigma_hat is generated and the KBQD test U-statistic for Normality is computed. After B iterations, the critical value is selected as the Quantile of the empirical distribution of the computed test statistics.

Value

the critical value for the specified dimension, size and level.

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