Confidence intervals for large non- centrality parameter

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dc.contributor.authorInácio, S.
dc.contributor.authorOliveira, M.
dc.contributor.authorMexia, J.T.
dc.contributor.editorZmyślony, R.
dc.date.accessioned2017-01-09T15:07:07Z
dc.date.available2017-01-09T15:07:07Z
dc.date.issued2015-05-02
dc.description.abstractWe use asymptotic linearity to derive confidence intervals for large noncentrality parameters. These results enable us to measure relevance of effects and interactions in multifactors models when we get highly statistically significant the values of F tests statistics. We show how to use our approach by considering two sets of data as application examples.por
dc.identifier.authoremailS.TimoteoInacio@brighton.ac.uk
dc.identifier.authoremailmmo@uevora.pt
dc.identifier.authoremailjtm@fct.unl.pt
dc.identifier.citationInácio, S. T., Oliveira, M. M., Mexia, J.T. 2015. Confidence intervals for large non-centrality parameter. Discussiones Mathematicae Probability and Statistics 35.45–56 doi:10.7151/dmps.1175. ISSN 1509-9423,ISSN 2084-0381.por
dc.identifier.doidoi:10.7151/dmps.1175por
dc.identifier.issn2084-0381
dc.identifier.scientificarea336por
dc.identifier.urihttp://hdl.handle.net/10174/19621
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherDiscussiones Mathematicae Probability and Statisticspor
dc.rightsrestrictedAccesspor
dc.subjectasymptotic linearitypor
dc.subjectnon-centrality parameterspor
dc.subjecthighly significant F testspor
dc.subjectmeasure relevancepor
dc.titleConfidence intervals for large non- centrality parameterpor
dc.typearticlepor
degois.publication.firstPage45por
degois.publication.lastPage56por
degois.publication.titleDiscussiones Mathematicae Probability and Statisticspor
degois.publication.volume35por

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