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dc.contributor.authorAdamidis, K.en
dc.contributor.authorLoukas, S.en
dc.date.accessioned2015-11-24T17:25:26Z-
dc.date.available2015-11-24T17:25:26Z-
dc.identifier.issn0094-9655-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/13042-
dc.rightsDefault Licence-
dc.subjectem algorithmen
dc.subjectgrouped dataen
dc.subjectneyman type a distributionen
dc.subjectpoisson binomial distributionen
dc.titleMl Estimation in the Poisson Binomial-Distribution with Grouped Data Via the Em Algorithmen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.secondary<Go to ISI>://A1993QM82000003-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μαθηματικώνel
heal.publicationDate1993-
heal.abstractThe maximum likelihood estimation of parameters of the Poisson binomial distribution, based on a sample with exact and grouped observations, is considered by applying the EM algorithm (Dempster et al., 1977). The results of Louis (1982) are used in obtaining the observed information matrix and accelerating the convergence of the EM algorithm substantially. The maximum likelihood estimation from samples consisting entirely of complete (Sprott, 1958) or grouped observations are treated as special cases of the estimation problem mentioned above. A brief account is given for the implementation of the EM algorithm when the sampling distribution is the Neyman Type A since the latter is a limiting form of the Poisson binomial. Numerical examples based on real data are included.en
heal.publisherTaylor & Francisen
heal.journalNameJournal of Statistical Computation and Simulationen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά). ΜΑΘ

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