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dc.contributor.authorTandoğdu, Y.
dc.contributor.authorCidar, Ö.
dc.date.accessioned2020-09-08T07:15:03Z
dc.date.available2020-09-08T07:15:03Z
dc.date.issued2009en_US
dc.identifier.citationTandoğdu, Y. ve Cidar, Ö. (2009). Smoothing the global mean based on functional principal component analysis. Maltepe Üniversitesi. s. 394.en_US
dc.identifier.isbn9786052124154
dc.identifier.urihttps://hdl.handle.net/20.500.12415/6197
dc.description.abstractThere are many cases in almost all application fields where the estimation of population parameters is carried out using sparse data. The data may be time or space ( r) dependent. When such data comes from a set of n trajectories (subjects), the Functional Principal Component Analysis (FPCA) is used to process the data for estimation purposes. In this study, the estimation and smoothing of global mean is considered.en_US
dc.language.isoengen_US
dc.publisherMaltepe Üniversitesien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectSmoothingen_US
dc.subjectFunctionalen_US
dc.subjectSparse dataen_US
dc.subjectCovarianceen_US
dc.titleSmoothing the global mean based on functional principal component analysisen_US
dc.typeconferenceObjecten_US
dc.relation.journalInternational Conference of Mathematical Sciencesen_US
dc.contributor.departmentMaltepe Üniversitesi, İnsan ve Toplum Bilimleri Fakültesien_US
dc.identifier.startpage394en_US
dc.identifier.endpage395en_US
dc.relation.publicationcategoryUluslararası Konferans Öğesien_US


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