Wilks' outlier test in more than one multivariate sample

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dc.contributor.author Caroni, C en
dc.date.accessioned 2014-03-01T01:14:16Z
dc.date.available 2014-03-01T01:14:16Z
dc.date.issued 1998 en
dc.identifier.issn 0361-0918 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/12970
dc.subject MANOVA en
dc.subject Multivariate normal en
dc.subject Outliers en
dc.subject Robustness en
dc.subject Wilks' test en
dc.subject.classification Statistics & Probability en
dc.title Wilks' outlier test in more than one multivariate sample en
heal.type journalArticle en
heal.identifier.primary 10.1080/03610919808813466 en
heal.identifier.secondary http://dx.doi.org/10.1080/03610919808813466 en
heal.language English en
heal.publicationDate 1998 en
heal.abstract Wilks' test for a single outlier in a multivariate normal sample is extended to the case of samples from different subpopulations with common covariance matrix, a situation arising in MANOVA, for example. Simulation results show that the size of the test is acceptably robust to moderate heterogeneity in covariances (25-50% difference in total variation), especially if sample sizes are small (below 20 per group). However covariance heterogeneity leads to a drastic loss of power, unless this heterogeneity is concentrated in one dimension and the outlier appears in a different dimension. It is concluded that the extended test should be used with caution since it will often be difficult to establish whether these conditions hold. en
heal.publisher MARCEL DEKKER INC en
heal.journalName Communications in Statistics Part B: Simulation and Computation en
dc.identifier.doi 10.1080/03610919808813466 en
dc.identifier.isi ISI:000072492100006 en
dc.identifier.volume 27 en
dc.identifier.issue 1 en
dc.identifier.spage 79 en
dc.identifier.epage 94 en

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