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dc.contributor.authorKankainen, Annaliisa
dc.contributor.authorTaskinen, Sara
dc.contributor.authorOja, Hannu
dc.date.accessioned2012-11-30T12:01:13Z
dc.date.available2012-11-30T12:01:13Z
dc.date.issued2007
dc.identifier.citationKankainen, A., Taskinen, S., & Oja, H. (2007). Tests of multinormality based on location vectors and scatter matrices. <i>Stat. Methods Appl.</i>, <i>16</i>(3), 357-379. <a href="https://doi.org/10.1007/s10260-007-0045-9" target="_blank">https://doi.org/10.1007/s10260-007-0045-9</a>
dc.identifier.otherCONVID_17461050
dc.identifier.otherTUTKAID_28711
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/40494
dc.description.abstractClassical univariate measures of asymmetry such as Pearson’s (mean-median)/σ or (mean-mode)/σ often measure the standardized distance between two separate location parameters and have been widely used in assessing univariate normality. Similarly, measures of univariate kurtosis are often just ratios of two scale measures. The classical standardized fourth moment and the ratio of the mean deviation to the standard deviation serve as examples. In this paper we consider tests of multinormality which are based on the Mahalanobis distance between two multivariate location vector estimates or on the (matrix) distance between two scatter matrix estimates, respectively. Asymptotic theory is developed to provide approximate null distributions as well as to consider asymptotic efficiencies. Limiting Pitman efficiencies for contiguous sequences of contaminated normal distributions are calculated and the efficiencies are compared to those of the classical tests by Mardia. Simulations are used to compare finite sample efficiencies. The theory is also illustrated by an example.fi
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofseriesStat. Methods Appl.
dc.subject.otherAffine invariance
dc.subject.otherKurtosis
dc.subject.otherPitman efficiency
dc.subject.otherSkewness
dc.titleTests of multinormality based on location vectors and scatter matrices
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201211293125
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineStatisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2012-11-29T10:40:04Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange357-379
dc.relation.issn1618-2510
dc.relation.numberinseries3
dc.relation.volume16
dc.type.versionsubmittedVersion
dc.rights.copyright© Springer. This is a manuscript of an article whose final and definitive form has been published by Springer.
dc.rights.accesslevelopenAccessfi
dc.relation.doi10.1007/s10260-007-0045-9
dc.type.okmA1


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