Tests for Determining if the Covariance Structure of 2-Dimensional Data is Separable
compute the difference between the full sample covariance and its sepa...
Projection-based empirical bootstrap test for separability of covarian...
Projection-based Gaussian (parametric) bootstrap test for separability...
Generate surface data
Empirical bootstrap test for separability of covariance structure usin...
Gaussian (parametric) bootstrap test for separability of covariance st...
estimates marginal covariances (e.g. row and column covariances) of bi...
Compute the projection of the rescaled difference between the sample c...
renormalize a matrix normal random matrix to have iid entries
Generate a sample from a Matrix Gaussian distribution
Test for separability of covariance operators for Gaussian process.
covsep: tests for determining if the covariance structure of 2-dimensi...
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>.