F. Abramovich, Y. Benjamini, D. Donoho, and I. Johnstone, Adapting to Unknown Sparsity by controlling the False Discovery Rate. The Annals of Statistics, pp.584-653, 2006.

H. Akaike, Statistical predictor identification, Annals of the Institute of Statistical Mathematics, vol.3, issue.1, pp.203-217, 1969.
DOI : 10.1007/BF02506337

H. Akaike, Information theory and an extension of the maximum likelihood principle, Second International Symposium on Information Theory (Tsahkadsor, 1971), pp.267-281, 1973.

M. David and . Allen, The relationship between variable selection and data augmentation and a method for prediction, Technometrics, vol.16, pp.125-127, 1974.

S. Arlot, Model selection by resampling penalization, Electronic Journal of Statistics, vol.3, issue.0, p.262478, 2008.
DOI : 10.1214/08-EJS196
URL : https://hal.archives-ouvertes.fr/hal-00125455

S. Arlot, Suboptimality of penalties proportional to the dimension for model selection in heteroscedastic regression, 2008.

S. Arlot, V -fold cross-validation improved: V -fold penalization, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00239182

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

Y. Baraud, Model selection for regression on a fixed design. Probab. Theory Related Fields, pp.467-493, 2000.

Y. Baraud, Model selection for regression on a random design, ESAIM: Probability and Statistics, vol.6, pp.127-146, 2002.
DOI : 10.1051/ps:2002007

A. Barron, L. Birgé, and P. Massart, Risk bounds for model selection via penalization, Probability Theory and Related Fields, vol.113, issue.3, pp.301-413, 1999.
DOI : 10.1007/s004400050210

M. Basseville and N. Nikiforov, The Detection of Abrupt Changes -Theory and Applications, Information and System Sciences Series, 1993.

R. E. Bellman and S. E. Dreyfus, Applied Dynamic Programming, 1962.
DOI : 10.1515/9781400874651

L. Birgé and P. Massart, From Model Selection to Adaptive Estimation, Festschrift for Lucien Le Cam: Research Papers in Probability and Statistics, pp.55-87, 1997.
DOI : 10.1007/978-1-4612-1880-7_4

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. Probab. Theory Related Fields, pp.33-73, 2007.

B. Brodsky and B. Darkhovsky, Methods in Change-point problems, 1993.

P. Burman, -fold cross-validation and the repeated learning-testing methods, Biometrika, vol.76, issue.3, pp.503-514, 1989.
DOI : 10.1093/biomet/76.3.503
URL : https://hal.archives-ouvertes.fr/hal-00819948

A. Celisse, Density estimation via cross-validation: Model selection point of view, 2008.

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

A. Celisse, Model selection via cross-validation in density estimation, regression and change-points detection, 2008.
URL : https://hal.archives-ouvertes.fr/tel-00346320

S. Dudoit and M. Van-der-laan, Asymptotics of cross-validated risk estimation in estimator selection and performance assessment, Statistical Methodology, vol.2, issue.2, pp.131-154, 2005.
DOI : 10.1016/j.stamet.2005.02.003

S. Geisser, A predictive approach to the random effect model, Biometrika, vol.61, issue.1, pp.101-107, 1974.
DOI : 10.1093/biomet/61.1.101

S. Geisser, The Predictive Sample Reuse Method with Applications, Journal of the American Statistical Association, vol.36, issue.2, pp.320-328, 1975.
DOI : 10.1080/01621459.1975.10479865

X. Gendre, Simultaneous estimation of the mean and the variance in heteroscedastic Gaussian regression, Electronic Journal of Statistics, vol.2, issue.0, 2008.
DOI : 10.1214/08-EJS267
URL : https://hal.archives-ouvertes.fr/hal-00298436

M. Kearns, Y. Mansour, A. Y. Ng, and D. Ron, An experimental and theoretical comparison of model selection methods, Proceedings of the eighth annual conference on Computational learning theory , COLT '95, pp.7-50, 1997.
DOI : 10.1145/225298.225301

P. A. Lachenbruch and M. R. Mickey, Estimation of Error Rates in Discriminant Analysis, Technometrics, vol.31, issue.1, pp.1-11, 1968.
DOI : 10.1080/00401706.1968.10490530

M. Lavielle, Using penalized contrasts for the change-point problem, Signal Processing, vol.85, issue.8, pp.1501-1510, 2005.
DOI : 10.1016/j.sigpro.2005.01.012
URL : https://hal.archives-ouvertes.fr/inria-00070662

M. Lavielle and G. Teyssì-ere, Detection of multiple change-points in multivariate time series, Lithuanian Mathematical Journal, vol.6, issue.2, pp.287-306, 2006.
DOI : 10.1007/s10986-006-0028-9

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

K. Li, Asymptotic Optimality for $C_p, C_L$, Cross-Validation and Generalized Cross-Validation: Discrete Index Set, The Annals of Statistics, vol.15, issue.3, pp.958-975, 1987.
DOI : 10.1214/aos/1176350486

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

B. Q. Mia and L. C. Zhao, On detection of change points when the number is unknown, Chinese J. Appl. Probab. Statist, vol.9, issue.2, pp.138-145, 1993.

D. Picard, Testing and Estimating Change-Points in Time Series, Advances in Applied Probability, vol.17, issue.4, pp.841-867, 1985.
DOI : 10.2307/1427090

F. Picard, Process segmentation/clustering Application to the analysis of array CGH data, 2005.

F. Picard, S. Robin, M. Lavielle, C. Vaisse, and J. Daudin, A statistical approach for array CGH data analysis, BMC Bioinformatics, vol.27, issue.6, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00427846

F. Picard, S. Robin, E. Lebarbier, and J. Daudin, A Segmentation/Clustering Model for the Analysis of Array CGH Data, Biometrics, vol.6, issue.3, 2007.
DOI : 10.1111/j.1541-0420.2006.00729.x
URL : https://hal.archives-ouvertes.fr/hal-01197574

J. Rissanen, A Universal Prior for Integers and Estimation by Minimum Description Length, The Annals of Statistics, vol.11, issue.2, pp.416-431, 1983.
DOI : 10.1214/aos/1176346150

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

J. Shao, An asymptotic theory for linear model selection, Statistica Sinica, vol.7, pp.221-264, 1997.

R. Shibata, An optimal selection of regression variables, Biometrika, vol.68, issue.1, pp.45-54, 1981.
DOI : 10.1093/biomet/68.1.45

C. J. Stone, An asymptotically optimal window selection rule for kernel density estimates . The Annals of Statistics, pp.1285-1297, 1984.

M. Stone, Cross-validatory choice and assessment of statistical predictions, J. Roy. Statist. Soc. Ser. B, vol.36, pp.111-147, 1974.

M. Stone, An Asymptotic Equivalence of Choice of Model by Cross-validation and Akaike's Criterion, JRSS B, vol.39, issue.1, pp.44-47, 1977.

R. Tibshirani and K. Knight, The Covariance Inflation Criterion for Adaptive Model Selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.3, pp.529-546, 1999.
DOI : 10.1111/1467-9868.00191

Y. Yang, Regression with multiple candidate model: selection or mixing? Statist. Sinica, pp.783-809, 2003.

Y. Yang, Comparing Learning Methods for Classification, Statistica Sinica, vol.16, pp.635-657, 2006.

Y. Yang, Consistency of cross-validation for comparing regression procedures. The Annals of Statistics, pp.2450-2473, 2007.

Y. Yao, Estimating the number of change-points via Schwarz' criterion, Statistics & Probability Letters, vol.6, issue.3, pp.181-189, 1988.
DOI : 10.1016/0167-7152(88)90118-6