Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/41047
Year of Publication: 
2009
Series/Report no.: 
Technical Report No. 2009,04
Publisher: 
Technische Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, Dortmund
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.
Subjects: 
irregular histogram
density estimation
penalized likelihood
dynamic programming
Document Type: 
Working Paper

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