An Omnibus Test for Univariate and Multivariate Normality
Jurgen A. Doornik,Henrik Hansen +1 more
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In this paper, a convenient version of the omnibus test for normality, using skewness and kurtosis based on Shenton and Bowman [Journal of the American Statistical Association (1977) Vol. 72, pp. 206 and 211], was proposed.Abstract:
We suggest a convenient version of the omnibus test for normality, using skewness and kurtosis based on Shenton and Bowman [Journal of the American Statistical Association (1977) Vol. 72, pp. 206–211], which controls well for size, for samples as low as 10 observations. A multivariate version is introduced. Size and power are investigated in comparison with four other tests for multivariate normality. The first power experiments consider the whole skewness–kurtosis plane; the second use a bivariate distribution which has normal marginals. It is concluded that the proposed test has the best size and power properties of the tests considered.read more
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References
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Measures of multivariate skewness and kurtosis with applications
TL;DR: In this article, the authors developed measures of multivariate skewness and kurtosis by extending certain studies on robustness of the t statistic, and the asymptotic distributions of the measures for samples from a multivariate normal population are derived and a test for multivariate normality is proposed.
Journal ArticleDOI
A test for normality of observations and regression residuals
Carlos M. Jarque,Anil K. Bera +1 more
TL;DR: In this paper, the Lagrange multiplier procedure or score test on the Pearson family of distributions was used to obtain tests for normality of observations and regression disturbances, and the tests suggested have optimum asymptotic power properties and good finite sample performance.
Book
Introduction to multiple time series analysis
TL;DR: The choice of point and interval forecasts as well as innovation accounting are presented as tools for structural analysis within the multiple time series context.
Journal ArticleDOI
A Suggestion for Using Powerful and Informative Tests of Normality
TL;DR: For testing that an underlying population is normally distributed the skewness and kurtosis statistics, √b 1 and b 2, and the D'Agostino-Pearson K 2 statistic that combines these two statistics have been shown to be powerful and informative tests.