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
Hauptverfasser: Székely, Gábor J., Rizzo, Maria L.
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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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