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dc.contributor.authorIntarapak S.
dc.contributor.authorSupapakorn T.
dc.date.accessioned2021-04-05T03:04:18Z-
dc.date.available2021-04-05T03:04:18Z-
dc.date.issued2019
dc.identifier.issn12347655
dc.identifier.other2-s2.0-85068326536
dc.identifier.urihttps://ir.swu.ac.th/jspui/handle/123456789/12584-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85068326536&doi=10.21307%2fstattrans-2019-009&partnerID=40&md5=402bbff615556eeec1a878a08a7f3595
dc.description.abstractFor the regression analysis of clustered data, the error of cluster data violates the independence assumption. Consequently, the test statistic based on the ordinary least square method leads to incorrect inferences. To overcome this issue, the transformation is required to apply to the observations. In this paper we propose an alternative matrix transformation that adjusts the intra-cluster correlation with Householder matrix and apply it to the F test statistic based on generalized least squares procedures for the regression coefficients hypothesis. By Monte Carlo simulations of the balanced and unbalanced data, it is found that the F test statistic based on generalized least squares procedures with Adjusted Householder transformation performs well in terms of the type I error rate and power of the test. © 2019 Glowny Urzad Statystyczny. All rights reserved.
dc.titleAn alternative matrix transformation to the F test statistic for clustered data
dc.typeArticle
dc.rights.holderScopus
dc.identifier.bibliograpycitationStatistics in Transition. Vol 20, No.1 (2019), p.153–169
dc.identifier.doi10.21307/stattrans-2019-009
Appears in Collections:Scopus 1983-2021

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