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Goodness-of-fit tests based on Rao's divergence under sparseness assumptions

dc.contributor.authorPardo Llorente, María del Carmen
dc.date.accessioned2023-06-20T17:06:47Z
dc.date.available2023-06-20T17:06:47Z
dc.date.issued2002-08
dc.descriptionThis work was supported by grant BMF 2000-0800.
dc.description.abstractIn many practical situations the classical (fixed-cells) assumptions to test goodness-of-fit are inappropriate, and we consider an alternative set of assumptions, which we call sparseness assumptions. It is proved that, under general conditions, the proposed family of statistics based on Rao's divergence is asymptotically normal when the sample size n and the number of cells Mn tend to infinity so that n/Mn→ v > 0. This result is extended to contiguous alternatives, and subsequently it is possible to find the asymptotically most efficient member of the family.
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.sponsorshipBMF
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/17603
dc.identifier.doi10.1016/S0096-3003(01)00095-9
dc.identifier.issn0096-3003
dc.identifier.officialurlhttp://www.sciencedirect.com/science/article/pii/S0096300301000959
dc.identifier.relatedurlhttp://www.sciencedirect.com/
dc.identifier.urihttps://hdl.handle.net/20.500.14352/57790
dc.issue.number2-3
dc.journal.titleApplied Mathematics and Computation
dc.language.isoeng
dc.page.final283
dc.page.initial265
dc.publisherElsevier
dc.relation.projectID2000-0800
dc.rights.accessRightsrestricted access
dc.subject.cdu519.22
dc.subject.keywordGoodness-of-fit test
dc.subject.keywordR/-divergence
dc.subject.keywordSparseness assumptions
dc.subject.keywordPower function
dc.subject.keywordEfficiency
dc.subject.ucmEstadística matemática (Matemáticas)
dc.subject.unesco1209 Estadística
dc.titleGoodness-of-fit tests based on Rao's divergence under sparseness assumptions
dc.typejournal article
dc.volume.number130
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