covsep (version 1.1.0)

HS_empirical_bootstrap_test: Empirical bootstrap test for separability of covariance structure using Hilbert--Schmidt distance

Description

Empirical bootstrap test for separability of covariance structure using Hilbert--Schmidt distance

Usage

HS_empirical_bootstrap_test(Data, B = 100, 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

# NOT RUN {
data(SurfacesData)
# }
# NOT RUN {
HS_empirical_bootstrap_test(SurfacesData)
# }
# NOT RUN {
HS_empirical_bootstrap_test(SurfacesData, B = 100)
# }