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dc.contributor.authorAydın, Dursun
dc.contributor.authorYılmaz, Ersin
dc.date.accessioned2020-11-20T14:54:41Z
dc.date.available2020-11-20T14:54:41Z
dc.date.issued2017
dc.identifier.issn1018-046X
dc.identifier.issn1844-7694
dc.identifier.urihttps://hdl.handle.net/20.500.12809/2164
dc.descriptionWOS: 000406282800005en_US
dc.description.abstractIn this paper, the proposed estimator for the unknown nonparametric regression function is a Nadarya-Watson (Nadarya, 1964; Watson, 1964) type kernel estimator. In this estimation procedure, the censored observations are replaced by synthetic data points based on Kaplan-Meier estimator. As known performance of the kernel estimator depends on the selection of a bandwidth parameter. To get an optimum parameter we have considered six selection methods such as the improved version of Akaike information criterion (AICc), Bayesian information criterion (BIC), generalized cross validation (GCV), risk estimation with classical pilots (RECP), Mallow's Cp criterion and restricted empirical likelihood (REML), respectively. In addition, we discuss the behavior of the estimators obtained by these selection methods under different confi gurations of the cens oring level and sample sizes. Simulation and real lifetime data results are presented to evaluate and compare the performance of the selectionen_US
dc.item-language.isoengen_US
dc.publisherNatl Inst Statisticsen_US
dc.item-rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectKernel Smoothingen_US
dc.subjectKaplan-Meier Estimatoren_US
dc.subjectNonparametric Regressionen_US
dc.subjectCensored Dataen_US
dc.titleBandwidth Selection Problem for Nonparametric Regression Model with Right-Censored Dataen_US
dc.item-typearticleen_US
dc.contributor.departmentMÜ, Fen Fakültesi, İstatistik Bölümüen_US
dc.contributor.institutionauthorAydın, Dursun
dc.contributor.institutionauthorYılmaz, Ersin
dc.identifier.issue2en_US
dc.identifier.startpage81en_US
dc.identifier.endpage104en_US
dc.relation.journalRomanian Statistical Reviewen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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