Gather, UrsulaMildenberger, ThoralfRozenholc, Yves2009-04-302009-04-302009-04-30http://hdl.handle.net/2003/26096http://dx.doi.org/10.17877/DE290R-587We 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 [1] and Massart [2]. 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. [1] Castellan, G., 1999. Modified Akaike's criterion for histogram density estimation. Technical Report 99.61, Université de Paris-Sud. [2] Massart, P., 2007. Concentration inequalities and model selection. Lecture Notes in Mathematics Vol. 1896, Springer, New York.endensity estimationdynamic programmingirregular histogrampenalized likelihood004Text