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dc.contributor.authorKent, J. T.
dc.date.accessioned2019-10-20T09:31:46Z
dc.date.available2019-10-20T09:31:46Z
dc.date.issued2015
dc.identifier.isbn9783319224046 -- 9783319224039
dc.identifier.urihttps://dx.doi.org/10.1007/978-3-319-22404-6_12
dc.identifier.urihttps://hdl.handle.net/11421/17782
dc.description.abstractThe spatial median can be defined as the unique minimum of a strictly convex objective function. Hence, its computation through an iterative algorithm ought to be straightforward. The simplest algorithm is the steepest descent Weiszfeld algorithm, as modified by Ostresh and by Vardi and Zhang. Another natural algorithm is Newton-Raphson. Unfortunately, all these algorithms can have problems near data points; indeed, Newton-Raphson can converge to a nonoptimal data point, even if a line search is included! However, by combining these algorithms, a reliable and efficient “hybrid” algorithm can be developeden_US
dc.language.isoengen_US
dc.publisherSpringer International Publishingen_US
dc.relation.isversionof10.1007/978-3-319-22404-6_12en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEm Algorithmen_US
dc.subjectMm Algorithmen_US
dc.subjectNewton-Raphson Algorithmen_US
dc.subjectSteepest Descenten_US
dc.subjectVardi-Zhang Algorithmen_US
dc.subjectWeiszfeld Algorithmen_US
dc.titleAlgorithms for the spatial medianen_US
dc.typebookParten_US
dc.relation.journalModern Nonparametric, Robust and Multivariate Methods: Festschrift in Honour of Hannu Ojaen_US
dc.contributor.departmentAnadolu Üniversitesi, Fen Fakültesi, İstatistik Bölümüen_US
dc.identifier.startpage205en_US
dc.identifier.endpage224en_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US


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