A new test for multivariate normality
We propose a new class of rotation invariant and consistent goodness-of-fit tests for multivariate distributions based on Euclidean distance between sample elements. The proposed test applies to any multivariate distribution with finite second moments. In this article we apply the new method for tes...
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Veröffentlicht in: | Journal of multivariate analysis 2005-03, Vol.93 (1), p.58-80 |
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container_title | Journal of multivariate analysis |
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creator | Székely, Gábor J. Rizzo, Maria L. |
description | We propose a new class of rotation invariant and consistent goodness-of-fit tests for multivariate distributions based on Euclidean distance between sample elements. The proposed test applies to any multivariate distribution with finite second moments. In this article we apply the new method for testing multivariate normality when parameters are estimated. The resulting test is affine invariant and consistent against all fixed alternatives. A comparative Monte Carlo study suggests that our test is a powerful competitor to existing tests, and is very sensitive against heavy tailed alternatives. |
doi_str_mv | 10.1016/j.jmva.2003.12.002 |
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subjects | BHEP test Distribution Exact sciences and technology Goodness-of-fit Goodness-of-fit Strictly negative definite BHEP test Henze-Zirkler test Multivariate skewness Multivariate kurtosis Projection pursuit Henze–Zirkler test Mathematical models Mathematics Monte Carlo simulation Multivariate analysis Multivariate kurtosis Multivariate skewness Nonparametric inference Probability and statistics Projection pursuit Sciences and techniques of general use Statistics Strictly negative definite Studies Tests |
title | A new test for multivariate normality |
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