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dc.contributor.authorDoronin, Oleksii
dc.contributor.authorMaiboroda, Rostislav
dc.date.accessioned2015-12-09T09:08:11Z
dc.date.available2015-12-09T09:08:11Z
dc.date.issued2015
dc.identifier.issn0208-6018
dc.identifier.urihttp://hdl.handle.net/11089/15310
dc.description.abstractWe discuss a semiparametric mixture model where some components are parameterized with common Euclidean parameter and others are fully unknown. We introduce GEE (generalized estimating equations) approach and adaptive GEE-based approach for parameter estimation. Derived estimators are consistent and asymptotically normal, and they are optimized in terms of their dispersion matrices. Proposed techniques are tested on simulated samples.pl_PL
dc.description.abstractW pracy omówiono semiparametryczny model mieszany, w którym pewne współczynniki są parametryzowane za pomocą wspólnego parametru euklidesowego, natomiast inne są zupełnie nieznane. Wprowadzono metodę estymacji parametrów opartą na podejściu GEE (uogólnionych równań estymujących) oraz adaptacyjnym podejściu GEE. Proponowane estymatory zostały przeanalizowane w badaniu symulacyjnym.pl_PL
dc.language.isoenpl_PL
dc.publisherWydawnictwo Uniwersytetu Łódzkiegopl_PL
dc.relation.ispartofseriesActa Universitatis Lodziensis. Folia Oeconomica;314
dc.subjectmixture modelpl_PL
dc.subjectsemiparametric estimationpl_PL
dc.subjectGEEpl_PL
dc.subjectmodel mieszanypl_PL
dc.subjectestymacja semiparametrycznapl_PL
dc.titleGEE Estimators in Mixture Model with Varying Concentrationspl_PL
dc.title.alternativeEstymatory GEE w modelu mieszanym ze zmiennymi współczynnikami koncentracjipl_PL
dc.typeArticlepl_PL
dc.rights.holder© Copyright by Uniwersytet Łódzki, Łódź 2015pl_PL
dc.page.number[15]-22pl_PL
dc.contributor.authorAffiliationDepartment of Probability Theory, Statistics and Actuarial Mathematics, Mechanics and Mathematics Faculty, Taras Shevchenko National University of Kyivpl_PL
dc.identifier.eissn2353-7663
dc.referencesDoronin O. (2014a), Lower bound of dispersion matrix for semiparametric estimation in mixture model. "Theory of Probability and Mathematical Statistics", no. 90, p. 64−76.pl_PL
dc.referencesDoronin O. (2014b), Adaptive estimation in semiparametric model of mixture with varying concentrations. "Theory of Probability and Mathematical Statistics", no. 91, p. 27−38.pl_PL
dc.referencesDoronin O. (2012), Robust Estimates for Mixtures with Gaussian Component. "Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics & Mathematics" (in Ukrainian), vol. 1, p. 18–23.pl_PL
dc.referencesMaiboroda R., Sugakova O. (2008), Estimation and classification by observations from mixtures. Kyiv University Publishers, Kyiv (in Ukrainian).pl_PL
dc.referencesMaiboroda R., Kubaichuk O. (2005), Improved estimators for moments constructed from observations of a mixture. "Theory of Probability and Mathematical Statistics", no. 70, p. 83−92.pl_PL
dc.referencesMaiboroda R., Sugakova O., Doronin A. (2013), Generalized estimating equations for mixtures with varying concentrations. "The Canadian Journal of Statistics", no. 41, vol. 2, p. 217−236.pl_PL
dc.identifier.doi10.18778/0208-6018.314.03
dc.relation.volume3pl_PL


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