H. Akaike, Information theory and an extension of the maximum likelihood principle, Proceedings, 2nd Internat. Symp. on Information Theory, pp.267-281, 1973.

C. Arlot and A. Celisse, A survey of cross-validation procedures for model selection, Statistics Surveys, vol.4, issue.0, 2009.
DOI : 10.1214/09-SS054
URL : https://hal.archives-ouvertes.fr/hal-00407906

S. Arlot, Model selection by resampling penalization, Electronic Journal of Statistics, vol.3, issue.0, pp.557-624, 2009.
DOI : 10.1214/08-EJS196
URL : https://hal.archives-ouvertes.fr/hal-00125455

S. Arlot and F. Bach, Data-driven calibration of linear estimators with minimal penalties, Advances in Neural Information Processing Systems (NIPS) 22, pp.46-54, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00414774

S. Arlot and P. Massart, Data-driven calibration of penalties for leastsquares regression, Journal of Machine Learning Research, vol.10, pp.245-279, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00287631

J. Baudry, G. Celeux, M. , and J. , Selecting Models Focussing on the Modeller???s Purpose, COMPSTAT 2008: Proceedings in Computational Statistics, pp.337-348, 2008.
DOI : 10.1007/978-3-7908-2084-3_28

C. Biernacki, G. Celeux, G. Govaert, and F. Langrognet, Model-based cluster and discriminant analysis with the MIXMOD software, Computational Statistics & Data Analysis, vol.51, issue.2, pp.587-600, 2006.
DOI : 10.1016/j.csda.2005.12.015
URL : https://hal.archives-ouvertes.fr/inria-00069878

L. Birgé and P. Massart, Gaussian model selection, Journal of the European Mathematical Society, vol.3, issue.3, pp.203-268, 2001.
DOI : 10.1007/s100970100031

L. Birgé and P. Massart, Minimal penalties for gaussian model selection. Probability Theory and Related Fields, pp.33-73, 2006.

K. Burnham and D. Anderson, Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2002.
DOI : 10.1007/b97636

C. Caillerie and B. Michel, Model Selection for Simplicial Approximation, Foundations of Computational Mathematics, vol.33, issue.2, 2009.
DOI : 10.1007/s10208-011-9103-7
URL : https://hal.archives-ouvertes.fr/inria-00402091

G. Castellan, S??lection d'histogrammes ?? l'aide d'un crit??re de type Akaike, Comptes Rendus de l'Acad??mie des Sciences - Series I - Mathematics, vol.330, issue.8, pp.729-732, 2000.
DOI : 10.1016/S0764-4442(00)00250-0

P. Craven and G. Wahba, Smoothing noisy data with spline functions, Numerische Mathematik, vol.4, issue.4, pp.377-403, 1978.
DOI : 10.1007/BF01404567

M. Denis and N. Molinari, Choix du nombre de noeuds en régression spline par l'heuristique des pentes, 41èmes Journées de Statistique, 2009.

C. Fraley and A. E. Raftery, Enhanced Model-Based Clustering, Density Estimation, and Discriminant Analysis Software: MCLUST, Journal of Classification, vol.20, issue.2, pp.263-286, 2003.
DOI : 10.1007/s00357-003-0015-3

P. J. Huber, Robust Statistics, 1981.

E. Lebarbier, Detecting multiple change-points in the mean of Gaussian process by model selection, Signal Processing, vol.85, issue.4, pp.717-736, 2005.
DOI : 10.1016/j.sigpro.2004.11.012
URL : https://hal.archives-ouvertes.fr/inria-00071847

V. Lepez, Some estimation problems related to oil reserves, 2002.
URL : https://hal.archives-ouvertes.fr/tel-00460802

M. Lerasle, Adaptive density estimation of stationary ??-mixing and ??-mixing processes, Mathematical Methods of Statistics, vol.18, issue.1, pp.59-83, 2009.
DOI : 10.3103/S1066530709010049

M. Lerasle, Optimal model selection in density estimation, Annales de l'Institut Henri Poincar??, Probabilit??s et Statistiques, vol.48, issue.3, 2009.
DOI : 10.1214/11-AIHP425
URL : https://hal.archives-ouvertes.fr/hal-00422655

M. Lerasle, Rééchantillonnage et sélection de modèles optimale pour l'estimation de la densité, 2009.

C. L. Mallows, Some comments on cp, Technometrics, vol.15, issue.4, pp.661-675, 1973.

P. Massart, Concentration Inequalities and Model Selection. ´ Ecole d'´ eté de Probabilités de Saint-Flour, Lecture Notes in Mathematics, 2003.

C. Maugis, G. Celeux, and M. Martin-magniette, Variable Selection for Clustering with Gaussian Mixture Models, Biometrics, vol.100, issue.3, pp.701-709, 2009.
DOI : 10.1111/j.1541-0420.2008.01160.x
URL : https://hal.archives-ouvertes.fr/inria-00153057

C. Maugis and B. Michel, A non asymptotic penalized criterion for Gaussian mixture model selection. ESAIM: P & S, 2009.
DOI : 10.1051/ps/2009004
URL : https://hal.archives-ouvertes.fr/inria-00284613

C. Maugis and B. Michel, Data-driven penalty calibration: A case study for Gaussian mixture model selection, ESAIM: Probability and Statistics, vol.15, 2010.
DOI : 10.1051/ps/2010002
URL : https://hal.archives-ouvertes.fr/hal-00666813

G. Schwarz, Estimating the dimension of a model. The annals of statistics, pp.461-464, 1978.

N. Verzelen, Data-driven neighborhood selection of a Gaussian field, Computational Statistics & Data Analysis, vol.54, issue.5, 2009.
DOI : 10.1016/j.csda.2009.12.001
URL : https://hal.archives-ouvertes.fr/inria-00353260

F. Villers, Tests et sélection de modèles pour l'analyse de données protéomiques et transcriptomiques, 2007.