Accessibility navigation


Testing normality of data on a multivariate grid

Horváth, L., Kokoszka, P. and Wang, S. (2020) Testing normality of data on a multivariate grid. Journal of Multivariate Analysis, 179. 104640. ISSN 0047-259X

[img] Text - Accepted Version
· Restricted to Registered users only until 28 May 2021.
· Available under License Creative Commons Attribution Non-commercial No Derivatives.
· Please see our End User Agreement before downloading.

599kB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1016/j.jmva.2020.104640

Abstract/Summary

We propose a significance test to determine if data on a regular d-dimensional grid can be assumed to be a realization of Gaussian process. By accounting for the spatial dependence of the observations, we derive statistics analogous to sample skewness and kurtosis. We show that the sum of squares of these two statistics converges to a chi-square distribution with two degrees of freedom. This leads to a readily applicable test. We examine two variants of the test, which are specified by two ways the spatial dependence is estimated. We provide a careful theoretical analysis, which justifies the validity of the test for a broad class of stationary random fields. A simulation study compares several implementations. While some implementations perform slightly better than others, all of them exhibit very good size control and high power, even in relatively small samples. An application to a comprehensive data set of sea surface temperatures further illustrates the usefulness of the test.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Arts, Humanities and Social Science > School of Politics, Economics and International Relations > Economics
ID Code:90726
Publisher:Elsevier

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation