Show simple item record

dc.contributor.authorNafiu, Lukman Abiodun
dc.contributor.authorIbitayo, Lanlege David
dc.contributor.authorMuyombya, Solomon Matovu
dc.date.accessioned2018-07-26T10:40:55Z
dc.date.available2018-07-26T10:40:55Z
dc.date.issued2017
dc.identifier.citationNafiu, L.A; Ibitayo, L.D; Muyombya, S.M (2017) On empirical power of univariate normality tests under symmetric, asymmetric, and scaled distributions. Pacific journal of science and technology. Vol. 18(1)en_US
dc.identifier.issn1551-7624
dc.identifier.urihttp://hdl.handle.net/20.500.12309/563
dc.description.abstractThe study aims at conducting an empirical comparison of powers of the univariate normality tests under different distributions to obtain their ranking using a Monte-Carlo simulation for large sample sizes. A total of six normality tests were selected. From the Empirical Distribution Function (EDF), the Kolmogorov-Smirnov (Lilliefors correction) and Anderson-Darling normality tests were chosen. From the regression and correlation family of distributions, the Shapiro-Wilk and Shapiro-Francia normality tests were chosen. The Jaque-Bera and D’Agostino Pearson normality tests were chosen from the moment family. The empirical powers of these normality tests were studied using distributions that are symmetric, asymmetric and scale contaminated normal distributions. Findings show that for symmetric distributions, Kolmogorov-Smirnov normality test is the most powerful test, followed by Anderson-Darling, Shapiro-Wilk, Shapiro-Francia, D'Agostino-Pearson and lastly Jaque-Bera. For asymmetric distributions, the Anderson-Darling normality test was best, followed by Shapiro-Wilk, Kolmogorov-Smirnov, Jaque-Bera, Shapiro-Francia and lastly D’Agostino. For scale contaminated distributions, Kolmogorov-Smirnov is the most powerful test, followed by Anderson-Darling, Shapiro-Francia, Shapiro-Wilk, D'Agostino-Pearson and lastly Jaque-Bera. Thus, regardless of the nature of the distribution given a large sample size, Kolmogorov-Smirnov is the most powerful normality test, followed by Shapiro-Wilk, Shapiro-Francia, Anderson-Darling, Jaque-Bera and lastly D'Agostino-Pearson. The study recommends that for distributions that have short tails like symmetric distributions, correlation/regression-based tests should be used. For long tailed distributions like symmetric distributions, Empirical-based normality tests should be used and moment-based tests should be used if interest is in kurtosis and skewness of the data.en_US
dc.language.isoenen_US
dc.publisherAkamai Universityen_US
dc.subjectUnivariate Normality Testsen_US
dc.subjectEmpirical distribution functionen_US
dc.subjectCorrelation/regression testsen_US
dc.subjectMoment-based normality testen_US
dc.subjectStatistical poweren_US
dc.titleOn empirical power of univariate normality tests under symmetric, asymmetric, and scaled distributionsen_US
dc.typeArticleen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record