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dc.contributor.authorKara, Mahmut
dc.contributor.authorAltındağ, Ömer
dc.contributor.authorPekalp, Mustafa Hilmi
dc.contributor.authorAydoğdu, Halil
dc.date.accessioned2019-12-27T18:11:05Z
dc.date.available2019-12-27T18:11:05Z
dc.date.issued2019en_US
dc.identifier.citationKara, M., Altındağ, Ö., Pekalp, M. H., & Aydoğdu, H. (2019). Parameter estimation in α-series process with lognormal distribution. Communications in Statistics-Theory and Methods, 48(20), 4976-4998.en_US
dc.identifier.issn0361-0926
dc.identifier.issn1532-415X
dc.identifier.urihttps://doi.org/10.1080/03610926.2018.1504075
dc.identifier.urihttps://hdl.handle.net/11552/490
dc.descriptionAltındağ, Ömer (Bilecik, Author)en_US
dc.description.abstractThe -series process (ASP) is widely used as a monotonic stochastic model in the reliability context. So the parameter estimation problem in an ASP is of importance. In this study parameter estimation problem for the ASP is considered when the distribution of the first occurrence time of an event is assumed to be lognormal. The parameters and of the ASP are estimated via maximum likelihood (ML) method. Asymptotic distributions and consistency properties of these estimators are derived. A test statistic is conducted to distinguish the ASP from renewal process (RP). Further, modified moment (MM) estimators are proposed for the parameters and and their consistency is proved. A nonparametric (NP) novel method is presented to test whether the ASP is a suitable model for data sets. Monte Carlo simulations are performed to compare the efficiencies of the ML and MM estimators. A real life data example is also studied to illustrate the usefulness of the ASP.The -series process (ASP) is widely used as a monotonic stochastic model in the reliability context. So the parameter estimation problem in an ASP is of importance. In this study parameter estimation problem for the ASP is considered when the distribution of the first occurrence time of an event is assumed to be lognormal. The parameters and of the ASP are estimated via maximum likelihood (ML) method. Asymptotic distributions and consistency properties of these estimators are derived. A test statistic is conducted to distinguish the ASP from renewal process (RP). Further, modified moment (MM) estimators are proposed for the parameters and and their consistency is proved. A nonparametric (NP) novel method is presented to test whether the ASP is a suitable model for data sets. Monte Carlo simulations are performed to compare the efficiencies of the ML and MM estimators. A real life data example is also studied to illustrate the usefulness of the ASP.en_US
dc.language.isoengen_US
dc.publisherTaylor & Francisen_US
dc.identifier.doi10.1080/03610926.2018.1504075en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectSeries processen_US
dc.subjectInferenceen_US
dc.subjectLognormal distributionen_US
dc.titleParameter estimation in alpha-series process with lognormal distributionen_US
dc.typearticleen_US
dc.relation.ispartofCommunicatıons in Statistics-Theory and Methodsen_US
dc.departmentFakülteler, Fen Edebiyat Fakültesi, İstatistik ve Bilgisayar Bilimleri Bölümüen_US
dc.authorid0000-0002-7035-9612en_US
dc.identifier.volume48en_US
dc.identifier.issue20en_US
dc.identifier.startpage4976en_US
dc.identifier.endpage4998en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.relation.indexScopusen_US
dc.relation.indexWoSen_US
dc.relation.indexWoS - Science Citation Index Expandeden_US
dc.contributor.institutionauthorAltındağ, Ömer
dc.description.wospublicationidWOS:000483669700002en_US
dc.description.wosqualityQ4en_US


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