HS_gaussian_bootstrap_test: Gaussian (parametric) bootstrap test for separability of...

In covsep: Tests for Determining if the Covariance Structure of 2-Dimensional Data is Separable

Description

Gaussian (parametric) bootstrap test for separability of covariance structure using Hilbert–Schmidt distance

Usage

HS_gaussian_bootstrap_test(Data, B = 1000, verbose = TRUE)

Arguments

Data	a (non-empty) N x d1 x d2 array of data values. The first direction indices the N observations, each consisting of a d1 x d2 discretization of the surface, so that Data[i,,] corresponds to the i-th observed surface.
B	number of bootstrap replicates to be used.
verbose	logical parameter for printing progress

Value

The p-value of the test.

Details

This function performs the test of separability of the covariance structure for a random surface (introduced in the paper http://arxiv.org/abs/1505.02023), when generated from a Gaussian process. The sample surfaces need to be measured on a common regular grid. The test considers the Hilbert–Schmidt distance between the sample covariance and its separable approximation. WE DO NOT RECOMMEND THIS TEST, as it is does not have the correct level, nor good power.

Examples

data(SurfacesData)
HS_gaussian_bootstrap_test(SurfacesData)
HS_gaussian_bootstrap_test(SurfacesData, B = 100)

covsep documentation built on May 2, 2019, 11:12 a.m.