Statistical inference for geometric process with the Rayleigh distribution

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dc.contributor.authorAydoğdu, Halil
dc.contributor.authorBiçer, Cenker
dc.contributor.authorKara, Mahmut
dc.contributor.authorBiçer, Hayrinisa Demirci
dc.contributor.departmentİstatistiktr_TR
dc.contributor.facultyFen Fakültesitr_TR
dc.date.accessioned2021-10-25T14:06:21Z
dc.date.available2021-10-25T14:06:21Z
dc.date.issued2019-02-01
dc.description.abstractThe aim of this study is to investigate the solution of the statistical inference problem for the geometric process (GP) when the distribution of first occurrence time is assumed to be Rayleigh. Maximum likelihood (ML) estimators for the parameters of GP, where a and λ are the ratio parameter of GP and scale parameter of Rayleigh distribution, respectively, are obtained. In addition, we derive some important asymptotic properties of these estimators such as normality and consistency. Then we run some simulation studies by different parameter values to compare the estimation performances of the obtained ML estimators with the non-parametric modified moment (MM) estimators. The results of the simulation studies show that the obtained estimators are more efficient than the MM estimators.tr_TR
dc.description.indexTrdizintr_TR
dc.identifier.endpage160tr_TR
dc.identifier.issn/e-issn2618-6470
dc.identifier.issue1tr_TR
dc.identifier.startpage149tr_TR
dc.identifier.urihttps://doi.org/10.31801/cfsuasmas.443690tr_TR
dc.identifier.urihttp://hdl.handle.net/20.500.12575/75736
dc.identifier.volume68tr_TR
dc.language.isoentr_TR
dc.publisherAnkara Üniversitesitr_TR
dc.relation.isversionof10.31801/cfsuasmas.443690tr_TR
dc.relation.journalCommunications Faculty of Sciences University of Ankara Series A1 Mathematics and Statisticstr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıtr_TR
dc.subjectParameter estimationtr_TR
dc.subjectGeometric processtr_TR
dc.subjectMaximum likelihood estimatorstr_TR
dc.titleStatistical inference for geometric process with the Rayleigh distributiontr_TR
dc.typeArticletr_TR

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Cilt:68 Sayı:01 (2019)