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dc.contributor.authorRozenholc, Yvesen_US
dc.contributor.authorMildenberger, Thoralfen_US
dc.contributor.authorGather, Ursulaen_US
dc.date.accessioned2009-09-02en_US
dc.date.accessioned2010-10-18T13:05:57Z-
dc.date.available2010-10-18T13:05:57Z-
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/10419/41047-
dc.description.abstractWe propose a fully automatic procedure for the construction of irregular histograms. For a given number of bins, the maximum likelihood histogram is known to be the result of a dynamic programming algorithm. To choose the number of bins, we propose two different penalties motivated by recent work in model selection by Castellan [6] and Massart [26]. We give a complete description of the algorithm and a proper tuning of the penalties. Finally, we compare our procedure to other existing proposals for a wide range of different densities and sample sizes.en_US
dc.language.isoengen_US
dc.publisherUniv., SFB 475 Dortmunden_US
dc.relation.ispartofseriesTechnical Report // Sonderforschungsbereich 475, Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund 2009,04en_US
dc.subject.ddc330en_US
dc.subject.keywordirregular histogramen_US
dc.subject.keyworddensity estimationen_US
dc.subject.keywordpenalized likelihooden_US
dc.subject.keyworddynamic programmingen_US
dc.titleConstructing irregular histograms by penalized likelihooden_US
dc.typeWorking Paperen_US
dc.identifier.ppn60804363Xen_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
dc.identifier.repecRePEc:zbw:sfb475:200904-
Appears in Collections:Technical Reports, SFB 475: Komplexitätsreduktion in multivariaten Datenstrukturen, TU Dortmund

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