Results 251 to 260 of about 93,664 (290)

Multivariate normality test using normalizing transformation for Mardia’s multivariate kurtosis

Communications in Statistics - Simulation and Computation, 2019
AbstractMultivariate skewness and kurtosis were defined by Mardia. However, the distribution of multivariate normality test statistics based on skewness and kurtosis is only obtainable for large sa...
Rie Enomoto, Zofia Hanusz, Ayako Hara, Takashi Seo   +3 more
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On Mardia’s kurtosis test for multivariate normality

Communications in Statistics - Theory and Methods, 1994
Let be independent identically distributed random(d-vectors with mean μ and nonsingular covariance matrix ∑ such that . We show that Mardia’s measure of multivariate kurtosis satisfies with σ2 depending on the distribution of X 1. As a consequence we obtain an approximation to the power function of a commonly proposed test for multivariate normality ...
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The asymptotic behavior of a variant of multivariate kurtosis

Communications in Statistics - Theory and Methods, 1994
Let be independent identically distributed random d-dimensional column vectors with arithmetic mean [Xbar] n and empirical covariance matrix S n. Apart from the celebrated kurtosis measure of Mardia, there has been recent interest in the variant which formally constitutes a closer analogue to the multivariate skewness measure , than b2,d . We show that,
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Estimating p-values for Mardia’s coefficients of multivariate skewness and kurtosis

Computational Statistics, 2002
This paper deals with a test of multivariate normality based on Mardia's estimates of multivariate skewness \(z_1\) and kurtosis \(z_2\). While the properly normalized statistics \(z_1\) and \(z_2\) asymptotically have a \(\chi^2\) and normal distribution, respectively, Mardia's test does not perform well in small sample cases. A Monte-Carlo method for
Douglas G. Bonett, J. Arthur Woodward, Robert L. Randall   +2 more
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Measures of multivariate skewness and kurtosis with applications

Biometrika, 1970
SUMMARY Measures of multivariate skewness and kurtosis are developed by extending certain studies on robustness of the t statistic. These measures are shown to possess desirable properties. The asymptotic distributions of the measures for samples from a multivariate normal population are derived and a test of multivariate normality is proposed.
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Effect of kurtosis on efficiency of some multivariate medians

Journal of Nonparametric Statistics, 2015
Up to now, various multivariate medians have been proposed. To support their applications, we study the effect of kurtosis on efficiency of some well-known multivariate medians. Results are established for the coordinatewise median, the spatial median, the Oja median, and their modified versions.
Jin Wang, Weihua Zhou
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Robust kurtosis projection for multivariate outlier labeling

2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2015
Outlier labeling can be considered as an early procedure to get the information of ‘suspects’. This paper introducesrobust kurtosis projection algorithm for multivariate outlier labeling of data set with moderate, high and very high percentage outlier. The algorithm works in two stages.
Dyah E. Herwindiati, Rahmat Sagara, Janson Hendryli   +2 more
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A comparison of tests for multivariate normality that are based on measures of multivariate skewness and kurtosis

Journal of Statistical Computation and Simulation, 1992
The examination of coefficients of multivariate skewness and kurtosis is one of the more commonly used techniques for assessing multivariate normality (MVN). In this article, several tests for MVN based on these coefficients are compared via Monte Carlo simulation.
Ronald L. Horswell, Stephen W. Looney
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Measures of multivariate skewness and kurtosis for tests of nonnormality

Statistical Papers, 1991
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lütkepohl, H., Theilen, B.
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A Measure of Multivariate Kurtosis with Principal Components

Communications in Statistics - Theory and Methods, 2008
Srivastava (1984) defined a measure of multivariate kurtosis and derived its asymptotic distribution for samples from a multivariate normal population. Some new results are obtained by generalizing Srivastava's theorem to an asymptotic expansion up to higher order. Finally, two numerical examples are presented.
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