difference_fullcov

<p>a (non-empty) <code>N x d1 x d2</code> array of data values. The first
direction indices the \(N\) observations, each consisting of a <code>d1 x d2</code>
discretization of the surface, so that <code>Data[i,,]</code> corresponds to the
i-th observed surface.</p>

Data

<p>compute the difference between the full sample covariance and its separable
approximation</p>

Functions for testing if the covariance structure of 2-dimensional data
(e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) =
C_1 x C_2.
A complete descriptions of the implemented tests can be found in the paper
Aston, John A. D.; Pigoli, Davide; Tavakoli, Shahin. Tests for separability in
nonparametric covariance operators of random surfaces. Ann. Statist. 45 (2017),
no. 4, 1431--1461. <doi:10.1214/16-AOS1495> <https://projecteuclid.org/euclid.aos/1498636862> <arXiv:1505.02023>.

Shahin Tavakoli

covsep

difference_fullcov: compute the difference between the full sample covariance and its separable approximation

Description

Usage

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Details