H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1973.
DOI : 10.1109/TAC.1974.1100705

A. Barron, L. Birgé, and P. Massart, Risk bounds for model selection via penalization. Probability Theory and Related Fields 113, pp.301-413, 1999.

A. Berlinet and L. Devroye, A comparison of kernel density estimates. Publications de l'Institut de Statistique de l, pp.3-59, 1994.

L. Birgé and Y. Rozenholc, How many bins should be put in a regular histogram? ESAIM: Probability and, Statistics, vol.10, pp.24-45, 2006.

G. Blanchard, C. Schäfer, Y. Rozenholc, and K. Müller, Optimal dyadic decision trees, Machine Learning, vol.52, issue.4, pp.209-241, 2007.
DOI : 10.1007/s10994-007-0717-6
URL : https://hal.archives-ouvertes.fr/hal-00264988

G. Castellan, Modified Akaike's criterion for histogram density estimation, 1999.

G. Castellan, Sélection d'histogrammesàhistogrammesà l'aide d'un critère de type Akaike. Comptes rendus de l'Académie des sciences Paris 330, Série I, pp.729-732, 2000.

O. Catoni, DATA COMPRESSION AND ADAPTIVE HISTOGRAMS, Foundations of Computational Mathematics, pp.35-60, 2000.
DOI : 10.1142/9789812778031_0003

A. Celisse and S. Robin, Nonparametric density estimation by exact leave--out cross-validation, Computational Statistics & Data Analysis, vol.52, issue.5, pp.2350-2368, 2008.
DOI : 10.1016/j.csda.2007.10.002
URL : https://hal.archives-ouvertes.fr/hal-01197590

X. R. Chen and L. C. Zhao, Almost sure L1-norm convergence for data-based histogram density estimates, Journal of Multivariate Analysis, vol.21, issue.1, pp.179-188, 1987.
DOI : 10.1016/0047-259X(87)90106-0
URL : http://doi.org/10.1016/0047-259x(87)90106-0

F. Comte and Y. Rozenholc, A new algorithm for fixed design regression and denoising, Annals of the Institute of Statistical Mathematics, vol.63, issue.2, pp.449-473, 2004.
DOI : 10.1007/BF02530536
URL : https://hal.archives-ouvertes.fr/hal-00748959

P. L. Davies, U. Gather, D. J. Nordman, and H. Weinert, A comparison of automatic histogram constructions, ESAIM: Probability and Statistics, vol.13, pp.181-196, 2009.
DOI : 10.1051/ps:2008005
URL : https://hal.archives-ouvertes.fr/hal-00491251

P. L. Davies and A. Kovac, Densities, spectral densities and modality, The Annals of Statistics, vol.32, pp.1093-1136, 2004.

P. L. Davies and A. Kovac, ftnonpar: Features and strings for nonparametric regression. R package version 0, pp.1-83, 2008.

L. Devroye and L. Györfi, Nonparametric density estimation: the L 1 view, 1985.

L. Devroye and G. Lugosi, Bin width selection in multivariate histograms by the combinatorial method, Test, vol.35, issue.1, pp.129-145, 2004.
DOI : 10.1007/BF02603004

J. Engel, The multiresolution histogram, Metrika, vol.51, issue.1, pp.41-57, 1997.
DOI : 10.1007/BF02717165

J. A. Hartigan, Bayesian histograms, Bayesian Statistics 5, pp.211-222, 1996.

Y. Kanazawa, An optimal variable cell histogram, Communications in Statistics - Theory and Methods, vol.66, issue.5, pp.1401-1422, 1988.
DOI : 10.1214/aos/1176350490

Y. Kanazawa, An Optimal Variable Cell Histogram Based on the Sample Spacings, The Annals of Statistics, vol.20, issue.1, pp.219-304, 1992.
DOI : 10.1214/aos/1176348523

J. Klemelä, Density estimation with stagewise optimization of the empirical risk, Machine Learning, vol.49, issue.6, pp.169-195, 2007.
DOI : 10.1007/s10994-006-5000-8

J. Klemelä, Multivariate histograms with data-dependent partitions, Statistica Sinica, vol.19, pp.159-176, 2009.

A. Kogure, Optimal cells for a histogram, 1986.

A. Kogure, Asymptotically Optimal Cells for a Historgram, The Annals of Statistics, vol.15, issue.3, pp.1023-1030, 1987.
DOI : 10.1214/aos/1176350490

P. Kontkanen and P. Myllymäki, MDL histogram density estimation, 2007.

G. Lugosi and A. Nobel, Consistency of data-driven histogram methods for density estimation and classification, The Annals of Statistics, vol.24, issue.2, pp.687-706, 1996.
DOI : 10.1214/aos/1032894460

P. Massart, Concentration inequalities and model selection, Lecture Notes in Mathematics, vol.1896, 2007.

T. Mildenberger, Y. Rozenholc, and D. Zasada, histogram: Construction of regular and irregular histograms with different options for automatic choice of bins. R package version 0, pp.0-23, 2009.

J. Rissanen, T. P. Speed, and B. Yu, Density estimation by stochastic complexity, IEEE Transactions on Information Theory, vol.38, issue.2, pp.315-323, 1992.
DOI : 10.1109/18.119689

Y. Rozenholc, T. Mildenberger, and U. Gather, Combining regular and irregular histograms by penalized likelihood. Discussion Paper 31, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00712352

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

L. C. Zhao, P. R. Krishnaiah, and X. R. Chen, Almost Sure $L_r$-Norm Convergence for Data-Based Histogram Density Estimates, Theory of Probability and its Applications, pp.396-403, 1988.
DOI : 10.1137/1135057