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dc.contributor.authorRozenholc, Yvesen_US
dc.contributor.authorMildenberger, Thoralfen_US
dc.contributor.authorGather, Ursulaen_US
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.publisher|aUniv., SFB 475 |cDortmunden_US
dc.relation.ispartofseries|aTechnical Report // Sonderforschungsbereich 475, Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund |x2009,04en_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.type|aWorking Paperen_US

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