T. Laframboise, Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances, Nucleic Acids Research, vol.37, issue.13, pp.4181-4193, 2009.
DOI : 10.1093/nar/gkp552

H. Bengtsson, K. Simpson, J. Bullard, and K. Hansen, aroma.affymetrix: A generic framework in R for analyzing small to very large Affymetrix data sets in bounded memory, 2008.

R. Killick and I. Eckley, Changepoint: An R Package for Changepoint Analysis, 2013.

M. Van-de-wiel and S. Vosse, CGHcall: Calling Aberrations for Array CGH Tumor Profiles. 2012. R package version 2.20

J. Friedman, T. Hastie, and R. Tibshirani, Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, vol.33, issue.1, pp.1-22, 2010.
DOI : 10.18637/jss.v033.i01

R. Team, R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing

H. Bengtsson, P. Neuvial, and T. Speed, TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays, BMC Bioinformatics, vol.11, issue.1, p.245, 2010.
DOI : 10.1186/1471-2105-11-245
URL : https://hal.archives-ouvertes.fr/hal-01199777

H. Bengtsson, P. Wirapati, and T. Speed, A single-array preprocessing method for estimating full-resolution raw copy numbers from all Affymetrix genotyping arrays including GenomeWideSNP 5 & 6, Bioinformatics, vol.25, issue.17, pp.252149-2156, 2009.
DOI : 10.1093/bioinformatics/btp371

T. Hocking, G. Schleiermacher, I. Janoueix-lerosey, V. Boeva, J. Cappo et al., Learning smoothing models of copy number profiles using breakpoint annotations, BMC Bioinformatics, vol.14, issue.1, p.164, 2013.
DOI : 10.1186/gb-2004-5-10-r80
URL : https://hal.archives-ouvertes.fr/hal-00663790

F. Picard, R. S. Lavielle, M. Vaisse, C. Daudin, and J. , A statistical approach for array CGH data analysis, BMC Bioinformatics, vol.6, issue.1, p.27, 2005.
DOI : 10.1186/1471-2105-6-27
URL : https://hal.archives-ouvertes.fr/hal-00427846

G. Rigaill, Pruned dynamic programming for optimal multiple change-point detection. arXiv e-print, 2010.

R. Killick, P. Fearnhead, and I. Eckley, Optimal Detection of Changepoints With a Linear Computational Cost, Journal of the American Statistical Association, vol.63, issue.500, pp.1590-1598
DOI : 10.1080/01621459.2012.737745

E. Lebarbier, Detecting multiple change-points in the mean of gaussian process by model selection. Signal Process, pp.717-736, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00071847

L. Birgé and P. Massart, Minimal penalties for gaussian model selection. Probability Theory Related Fields, pp.33-73, 2007.

J. Staaf, D. Lindgren, J. Vallon-christersson, A. Isaksson, H. Göransson et al., Segmentation-based detection of allelic imbalance and loss-of-heterozygosity in cancer cells using whole genome SNP arrays, Genome Biology, vol.9, issue.9, p.136, 2008.
DOI : 10.1186/gb-2008-9-9-r136

M. Van-de-wiel, K. Kim, S. Vosse, W. Van-wieringen, S. Wilting et al., CGHcall: calling aberrations for array CGH tumor profiles, Bioinformatics, vol.23, issue.7, pp.892-894, 2007.
DOI : 10.1093/bioinformatics/btm030

R. Tibshirani, Regression shrinkage and selection via the lasso, J R Stat Soci Series B, vol.58, pp.267-288, 1994.

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Ann Stat, vol.32, pp.407-499, 2004.

P. Ravikumar, W. M. Raskutti, G. , and Y. B. , High-dimensional covariance estimation by minimizing ???1-penalized log-determinant divergence, Electronic Journal of Statistics, vol.5, issue.0, pp.935-980, 2011.
DOI : 10.1214/11-EJS631

H. Ishwaran and J. Rao, Spike and slab variable selection: Frequentist and Bayesian strategies, The Annals of Statistics, vol.33, issue.2, pp.730-773, 2005.
DOI : 10.1214/009053604000001147

H. Ishwaran and J. Rao, Generalized ridge regression: geometry and computational solutions when p is larger than n, 2010.

S. Arlot and C. A. , A survey of cross-validation procedures for model selection, Statistics Surveys, vol.4, issue.0, pp.40-79, 2010.
DOI : 10.1214/09-SS054
URL : https://hal.archives-ouvertes.fr/hal-00407906

F. Anscombe, THE TRANSFORMATION OF POISSON, BINOMIAL AND NEGATIVE-BINOMIAL DATA, Biometrika, vol.35, issue.3-4, pp.246-254, 1948.
DOI : 10.1093/biomet/35.3-4.246