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dc.contributor.authorOpheim, Timothy
dc.contributor.authorRoy, Anuradha
dc.date.accessioned2021-12-23T15:06:36Z
dc.date.available2021-12-23T15:06:36Z
dc.date.issued2021-11-29
dc.identifierdoi: 10.3390/computation9120126
dc.identifier.citationComputation 9 (12): 126 (2021)
dc.identifier.urihttps://hdl.handle.net/20.500.12588/779
dc.description.abstractThis review is about verifying and generalizing the supremum test statistic developed by Balakrishnan et al. Exhaustive simulation studies are conducted for various dimensions to determine the effect, in terms of empirical size, of the supremum test statistic developed by Balakrishnan et al. to test multivariate skew-normality. Monte Carlo simulation studies indicate that the Type-I error of the supremum test can be controlled reasonably well for various dimensions for given nominal significance levels 0.05 and 0.01. Cut-off values are provided for the number of samples required to attain the nominal significance levels 0.05 and 0.01. Some new and relevant information of the supremum test statistic are reported here.
dc.titleMore on the Supremum Statistic to Test Multivariate Skew-Normality
dc.date.updated2021-12-23T15:06:37Z
dc.description.departmentManagement Science and Statistics


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