Multivariate Normality test for colored data

TL;DR

This paper investigates the effectiveness of the Multivariate Kurtosis test for colored data, examining the impact of pre-whitening and dimensionality reduction on test performance through computer experiments.

Contribution

It provides a comprehensive analysis of how the color of data affects the power of the Multivariate Kurtosis normality test, highlighting the importance of accounting for data color.

Findings

Color of data significantly influences test power

Pre-whitening can improve test accuracy

Dimensionality reduction impacts test performance

Abstract

Performances of the Multivariate Kurtosis are investigated when applied to colored data, with or without Auto-Regressive pre-whitening, and with or without projection onto a lower-dimensional random subspace. Computer experiments demonstrate the importance of taking into account the possible color of the process in calculating the power of the normality test, in all the scenarios.