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covsep (version 1.1.0)

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

Description

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. .

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Version

Install

install.packages('covsep')

Monthly Downloads

182

Version

1.1.0

License

GPL-2

Maintainer

Shahin Tavakoli

Last Published

May 6th, 2018

Functions in covsep (1.1.0)

gaussian_bootstrap_test

Projection-based Gaussian (parametric) bootstrap test for separability of covariance structure
difference_fullcov

compute the difference between the full sample covariance and its separable approximation
HS_gaussian_bootstrap_test

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

Test for separability of covariance operators for Gaussian process.
HS_empirical_bootstrap_test

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

Projection-based empirical bootstrap test for separability of covariance structure
SurfacesData

A data set of surfaces
renormalize_mtnorm

renormalize a matrix normal random matrix to have iid entries
covsep

covsep: tests for determining if the covariance structure of 2-dimensional data is separable
C2

A covariance matrix
C1

A covariance matrix
marginal_covariances

estimates marginal covariances (e.g. row and column covariances) of bi-dimensional sample
generate_surface_data

Generate surface data
projected_differences

Compute the projection of the rescaled difference between the sample covariance and its separable approximation onto the separable eigenfunctions
rmtnorm

Generate a sample from a Matrix Gaussian distribution