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A comparison of some estimators of the mixture proportion of mixed normal distributions

dc.contributor.authorPardo Llorente, María del Carmen
dc.date.accessioned2023-06-20T17:08:34Z
dc.date.available2023-06-20T17:08:34Z
dc.date.issued1997-10-28
dc.descriptionThis work was supported by Grant DGICYT PB94-0308
dc.description.abstractFisher's method of maximum likelihood breaks down when applied to the problem of estimating the five parameters of a mixture of two normal densities from a continuous random sample of size n. Alternative methods based on minimum-distance estimation by grouping the underlying variable are proposed. Simulation results compare the efficiency as well as the robustness under symmetric departures from component normality of these estimators. Our results indicate that the estimator based on Rao's divergence is better than other classic ones.
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.sponsorshipDGICYT
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/17878
dc.identifier.doi10.1016/S0377-0427(97)00124-6
dc.identifier.issn0377-0427
dc.identifier.officialurlhttp://www.sciencedirect.com/science/article/pii/S0377042797001246
dc.identifier.relatedurlhttp://www.sciencedirect.com/
dc.identifier.urihttps://hdl.handle.net/20.500.14352/57835
dc.issue.number2
dc.journal.titleJournal of Computational and Applied Mathematics
dc.language.isoeng
dc.page.final217
dc.page.initial207
dc.publisherElsevier Science Bv
dc.relation.projectIDPB94-0308
dc.rights.accessRightsopen access
dc.subject.cdu519.22
dc.subject.keywordMinimum-distance estimator
dc.subject.keywordSimulation
dc.subject.keywordRelative efficiency
dc.subject.ucmEstadística matemática (Matemáticas)
dc.subject.unesco1209 Estadística
dc.titleA comparison of some estimators of the mixture proportion of mixed normal distributions
dc.typejournal article
dc.volume.number84
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