Are You All Normal? It Depends!

TL;DR

This paper introduces a new multivariate normality test tailored for spatial data, addressing the limitations of existing tests under dependence, and demonstrates its effectiveness through simulations and real wind data analysis.

Contribution

The paper proposes a novel multivariate normality test for spatial data that accounts for dependence, improving accuracy over existing methods.

Findings

The new test controls type I error effectively.

It exhibits high power in large samples.

Successfully applied to wind data over Arabian Peninsula.

Abstract

The assumption of normality has underlain much of the development of statistics, including spatial statistics, and many tests have been proposed. In this work, we focus on the multivariate setting and first review the recent advances in multivariate normality tests for i.i.d. data, with emphasis on the skewness and kurtosis approaches. We show through simulation studies that some of these tests cannot be used directly for testing normality of spatial data. We further review briefly the few existing univariate tests under dependence (time or space), and then propose a new multivariate normality test for spatial data by accounting for the spatial dependence. The new test utilizes the union-intersection principle to decompose the null hypothesis into intersections of univariate normality hypotheses for projection data, and it rejects the multivariate normality if any individual hypothesis is rejected. The individual hypotheses for univariate normality are conducted using a Jarque-Bera type test statistic that accounts for the spatial dependence in the data. We also show in simulation studies that the new test has a good control of the type I error and a high empirical power, especially for large sample sizes. We further illustrate our test on bivariate wind data over the Arabian Peninsula.