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Diagnostic limitations of skewness coefficients in assessing departures from univariate and multivariate normality

Diagnostic limitations of skewness coefficients in assessing departures from univariate and... While many tests of univariate and multivariate normality have been proposed, those based on skewness and kurtosis coefficients are widely presumed to offer the advantage of diagnosing how distributions depart from normality. However, results summarized from many Monte Carlo studies show that tests based on skewness coefficients do not reliably discriminate between skewed and non-skewed distributions. Indeed, the use of skewness tests to discriminate between these distributions lackstheoretical foundation. The performance of skewness tests is shown to be very sensitive to the kurtosis of the underlying distribution http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Communications in Statistics: Simulation and Computation Taylor & Francis

Diagnostic limitations of skewness coefficients in assessing departures from univariate and multivariate normality

Diagnostic limitations of skewness coefficients in assessing departures from univariate and multivariate normality

Communications in Statistics: Simulation and Computation , Volume 22 (2): 23 – Jan 1, 1993

Abstract

While many tests of univariate and multivariate normality have been proposed, those based on skewness and kurtosis coefficients are widely presumed to offer the advantage of diagnosing how distributions depart from normality. However, results summarized from many Monte Carlo studies show that tests based on skewness coefficients do not reliably discriminate between skewed and non-skewed distributions. Indeed, the use of skewness tests to discriminate between these distributions lackstheoretical foundation. The performance of skewness tests is shown to be very sensitive to the kurtosis of the underlying distribution

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References (19)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1532-4141
eISSN
0361-0918
DOI
10.1080/03610919308813102
Publisher site
See Article on Publisher Site

Abstract

While many tests of univariate and multivariate normality have been proposed, those based on skewness and kurtosis coefficients are widely presumed to offer the advantage of diagnosing how distributions depart from normality. However, results summarized from many Monte Carlo studies show that tests based on skewness coefficients do not reliably discriminate between skewed and non-skewed distributions. Indeed, the use of skewness tests to discriminate between these distributions lackstheoretical foundation. The performance of skewness tests is shown to be very sensitive to the kurtosis of the underlying distribution

Journal

Communications in Statistics: Simulation and ComputationTaylor & Francis

Published: Jan 1, 1993

Keywords: kurtosis; multivariate; normality; radii; skewness; univariate normality

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