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Rozenholc, Yves
Mildenberger, Thoralf
Gather, Ursula
Year of Publication: 
Series/Report no.: 
Technical Report 2009,04
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.
irregular histogram
density estimation
penalized likelihood
dynamic programming
Document Type: 
Working Paper

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