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https://hdl.handle.net/10419/41047
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Rozenholc, Yves | en |
dc.contributor.author | Mildenberger, Thoralf | en |
dc.contributor.author | Gather, Ursula | en |
dc.date.accessioned | 2009-09-02 | - |
dc.date.accessioned | 2010-10-18T13:05:57Z | - |
dc.date.available | 2010-10-18T13:05:57Z | - |
dc.date.issued | 2009 | - |
dc.identifier.uri | http://hdl.handle.net/10419/41047 | - |
dc.description.abstract | We 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 |
dc.language.iso | eng | en |
dc.publisher | |aTechnische Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen |cDortmund | en |
dc.relation.ispartofseries | |aTechnical Report |x2009,04 | en |
dc.subject.ddc | 519 | en |
dc.subject.keyword | irregular histogram | en |
dc.subject.keyword | density estimation | en |
dc.subject.keyword | penalized likelihood | en |
dc.subject.keyword | dynamic programming | en |
dc.title | Constructing irregular histograms by penalized likelihood | - |
dc.type | Working Paper | en |
dc.identifier.ppn | 60804363X | en |
dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | en |
dc.identifier.repec | RePEc:zbw:sfb475:200904 | en |
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