. Pour-un-bruit-symétrique, absence de valeurs aberrantes, l'efficacité asymptotique se ramène au ratio de la variance de l'estimateur LS divisé par la variance asymptotique de l'estimateur robuste. l'état de l'art, BEADS et LOWESS, en particulier dans la zone de la raie d'émission

. Le-coût-calculatoire-est-néanmoins-relativement-Élevé, quelques secondes pour un signal de 3600 valeurs dans son implémentation Python, contre 200ms pour l'implémentation MATLAB de BEADS et une seconde pour la version Python de LOWESS. Il est à noter toutefois que BEADS dépend de quatre paramètres : la fréquence de coupure et trois poids de pénalisations qui doivent être réglé avec précision

M. Aharon, M. Elad, and A. Bruckstein, $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4311-4322, 2006.
DOI : 10.1109/TSP.2006.881199

K. Aho, D. Derryberry, and T. Peterson, Model selection for ecologists: the worldviews of AIC and BIC, Ecology, vol.95, issue.3, pp.631-636, 2014.
DOI : 10.1093/biomet/92.4.937

H. Akaike, « A new look at the statistical model identification, IEEE transactions on automatic control, vol.196, pp.716-723, 1974.

H. Akaike, Maximum likelihood identification of Gaussian autoregressive moving average models, Biometrika 60, pp.255-265, 1973.
DOI : 10.1093/biomet/60.2.255

M. Akhlaghi and T. Ichikawa, NOISE-BASED DETECTION AND SEGMENTATION OF NEBULOUS OBJECTS, The Astrophysical Journal Supplement Series, vol.220, issue.1, pp.1-62, 2015.
DOI : 10.1088/0067-0049/220/1/1

. Arias-castro, E. J. Ery, Y. Candès, and . Plan, Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism, The Annals of Statistics, pp.2533-2556, 2011.
DOI : 10.1214/11-AOS910SUPP
URL : http://doi.org/10.1214/11-aos910

. Arlot, A. Sylvain, and . Celisse, 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

R. Bacher, F. Chatelain, and O. Michel, An adaptive robust regression method: Application to galaxy spectrum baseline estimation, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.135-145, 2016.
DOI : 10.1109/ICASSP.2016.7472513
URL : https://hal.archives-ouvertes.fr/hal-01462974

R. Bacher, P. Maho, F. Chatelain, and O. Michel, Tracking the Lyman alpha emission line in the CircumGalactic Medium in MUSE data, EAS Publications Series, vol.61, issue.79, pp.233-245, 2016.
DOI : 10.1093/mnras/stw474
URL : https://hal.archives-ouvertes.fr/hal-01503524

R. Bacher, F. Chatelain, and O. Michel, « Détection de cibles spatialement structurées sous contrôle global d'erreur, Colloque du Groupe de recherche et d'étude de traitement du signal et des images (GRETSI), p.2017, 2017.

R. Bacher, C. Meillier, F. Chatelain, and O. Michel, Robust Control of Varying Weak Hyperspectral Target Detection With Sparse Nonnegative Representation, IEEE Transactions on Signal Processing, vol.65, issue.13, pp.3538-3550, 2017.
DOI : 10.1109/TSP.2017.2688965
URL : https://hal.archives-ouvertes.fr/hal-01496201

R. Bacon, . Brinchmann, . Richard, . Contini, . Drake et al., Deep Field South, Astronomy & Astrophysics, vol.706, issue.149, pp.75-78, 2015.
DOI : 10.1088/0004-637X/706/1/885
URL : https://hal.archives-ouvertes.fr/hal-01439826

R. Bacon, S. Conseil, D. Mary, J. Brinchmann, M. Shepherd et al., Ultra Deep Field Survey, Astronomy and Astrophysics (cf, p.121, 2017.
DOI : 10.1088/0067-0049/195/1/10
URL : https://hal.archives-ouvertes.fr/hal-01678478

R. Bacon, L. Piqueras, S. Conseil, J. Richard, and M. Shepherd, « MPDAF : MUSE Python Data Analysis Framework ». In : Astrophysics Source Code Library (cf, p.128, 2016.

R. Barber, . Foygel, J. Emmanuel, and . Candès, Controlling the false discovery rate via knockoffs, The Annals of Statistics, vol.43, issue.5, pp.2055-2085, 2015.
DOI : 10.1214/15-AOS1337SUPP

R. Barber, A. Foygel, and . Ramdas, « The p-filter : multi-layer FDR control for grouped hypotheses, pp.83-98, 2015.

J. D. Bayliss, A. Gualtieri, F. Robert, and . Cromp, « Analyzing hyperspectral data with independent component analysis, 26th AIPR Workshop : Exploiting New Image Sources and Sensors. International Society for Optics et Photonics, pp.133-143, 1998.
DOI : 10.1117/12.300050
URL : http://www.cs.rochester.edu/u/www/u/bayliss/research/../spectral/aipr97.ps

A. Belloni and V. Chernozhukov, « Least squares after model selection in high-dimensional sparse models, p.49, 2009.

Y. Benjamini and Y. Hochberg, « Controlling the false discovery rate : a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society. Series B, vol.85, pp.289-300, 1995.

Y. Benjamini and D. Yekutieli, « The control of the false discovery rate in multiple testing under dependency, Annals of statistics, pp.1165-1188, 2001.

E. Bertin and S. Arnouts, SExtractor: Software for source extraction, Astronomy and Astrophysics Supplement Series 117.2, pp.393-404, 1996.
DOI : 10.1051/aas:1996164
URL : http://aas.aanda.org/articles/aas/pdf/1996/08/ds1060.pdf

S. Bourguignon, D. Mary, and É. Slezak, Restoration of Astrophysical Spectra With Sparsity Constraints: Models and Algorithms, IEEE Journal of Selected Topics in Signal Processing, vol.5, issue.5, pp.1002-1013, 2011.
DOI : 10.1109/JSTSP.2011.2147278
URL : https://hal.archives-ouvertes.fr/hal-00980638

L. Breiman, J. Friedman, J. Charles, . Stone, A. Richard et al., Classification and regression trees, pp.49-51, 1984.

E. Candès, Y. Fan, L. Janson, and J. Lv, Panning for gold: ???model-X??? knockoffs for high dimensional controlled variable selection, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.7, p.101, 2016.
DOI : 10.1093/bioinformatics/btp041

H. Carfantan, « Modèles, estimateurs et algorithmes pour quelques problèmes inverses de traitement du signal et d'images en sciences de l'univers, Habilitation à Diriger des Recherches (HDR) (cf, p.6, 2014.

C. Chang, Spectral information divergence for hyperspectral image analysis, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293), pp.509-511, 1999.
DOI : 10.1109/IGARSS.1999.773549

Y. Chen, M. Nasser, . Nasrabadi, D. Trac, and . Tran, « Sparse representation for target detection in hyperspectral imagery, IEEE Journal of Selected Topics in Signal Processing, vol.53, pp.629-640, 2011.

I. Chiu, S. Desai, and J. Liu, ComEst: A completeness estimator of source extraction on astronomical imaging, Astronomy and Computing, vol.16, pp.79-87, 2016.
DOI : 10.1016/j.ascom.2016.04.005

W. S. Cleveland, LOWESS: A Program for Smoothing Scatterplots by Robust Locally Weighted Regression, The American Statistician, vol.35, issue.1, pp.54-54, 1981.
DOI : 10.2307/2683591

P. Comon, Independent component analysis, A new concept?, Signal Processing, vol.36, issue.3, pp.287-314, 1994.
DOI : 10.1016/0165-1684(94)90029-9
URL : https://hal.archives-ouvertes.fr/hal-00417283

J. Courbot, « Détection de sources quasi-ponctuelles dans des champs de données massifs, Thèse de doct, 2017.

J. Courbot, E. Monfrini, V. Mazet, and C. Collet, Oriented Triplet Markov fields for hyperspectral image segmentation, 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp.79-125, 2016.
DOI : 10.1109/WHISPERS.2016.8071755

J. Courbot, V. Mazet, E. Monfrini, and C. Collet, Extended faint source detection in astronomical hyperspectral images, Signal Processing, vol.135, pp.274-283, 2017.
DOI : 10.1016/j.sigpro.2017.01.013
URL : https://hal.archives-ouvertes.fr/hal-01548163

D. Donoho and J. Jin, « Higher criticism for detecting sparse heterogeneous mixtures ». In : The Annals of Statistics 32, pp.962-994, 2004.

O. Dunn and . Jean, Multiple Comparisons among Means, Journal of the American Statistical Association, vol.25, issue.293, pp.52-64, 1961.
DOI : 10.1214/aoms/1177728724

B. Efron, Large-scale inference : empirical Bayes methods for estimation, testing, and prediction. T. 1, p.89, 2012.
DOI : 10.1017/CBO9780511761362

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, « Least angle regression, The Annals of statistics 32, pp.407-499, 2004.

J. D. Esary, F. Proschan, W. David, and . Walkup, Association of Random Variables, with Applications, The Annals of Mathematical Statistics, vol.38, issue.5, pp.1466-1474, 1967.
DOI : 10.1214/aoms/1177698701

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.1-44, 2010.
DOI : 10.18637/jss.v033.i01
URL : https://doi.org/10.18637/jss.v033.i01

A. Fruchter, « HST multidrizzle handbook, HST MultiDrizzle, HST Data Handbooks 1, 2009.

. Galatsanos, P. Nikolas, K. Aggelos, and . Katsaggelos, Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation, IEEE Transactions on Image Processing, vol.1, issue.3, pp.322-336, 1992.
DOI : 10.1109/83.148606

S. Geisser, The Predictive Sample Reuse Method with Applications, Journal of the American Statistical Association, vol.36, issue.2, pp.320-328, 1975.
DOI : 10.1007/BF02297848

C. R. Genovese, N. A. Lazar, and T. Nichols, Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate, NeuroImage, vol.15, issue.4, pp.870-878, 2002.
DOI : 10.1006/nimg.2001.1037

A. Genz and F. Bretz, Computation of multivariate normal and t probabilities. T. 195, p.114, 2009.

G. H. Golub, M. Heath, and G. Wahba, Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter, Technometrics, vol.5, issue.2, pp.215-223, 1979.
DOI : 10.1080/03610927508827223

P. Hall and J. Jin, Innovated higher criticism for detecting sparse signals in correlated noise, The Annals of Statistics, vol.38, issue.3, pp.1686-1732, 2010.
DOI : 10.1214/09-AOS764

D. M. Haughton, On the Choice of a Model to Fit Data from an Exponential Family, The Annals of Statistics, vol.16, issue.1, pp.342-355, 1988.
DOI : 10.1214/aos/1176350709

A. E. Hoerl, W. Robert, and . Kennard, « Ridge regression : Biased estimation for nonorthogonal problems, pp.55-67, 1970.
DOI : 10.2307/1271436

K. Huang and . Et-selin-aviyente, « Sparse representation for signal classification, Advances in Neural Information Processing Systems 19 :Proceedings of the NIPS, 2006.

J. P. Ioannidis, « Why most published research findings are false, PLoS medicine 2, pp.124-81, 2005.

N. Keshava, F. John, and . Mustard, Spectral unmixing, IEEE Signal Processing Magazine, vol.19, issue.1, pp.44-57, 2002.
DOI : 10.1109/79.974727

R. Kohavi, « A study of cross-validation and bootstrap for accuracy estimation and model selection, Ijcai. T. 14. 2. Stanford, CA, pp.1137-1145, 1995.

D. D. Lee and S. Seung, « Learning the parts of objects by non-negative matrix factorization, Nature, vol.4016755, pp.788-791, 1999.

A. Liddle, An introduction to modern cosmology, p.10, 2015.

L. Loncan, L. B. De-almeida, M. José, X. Bioucas-dias, J. Briottet et al., Hyperspectral Pansharpening: A Review, IEEE Geoscience and remote sensing magazine 3.3, pp.27-46, 2015.
DOI : 10.1109/MGRS.2015.2440094
URL : https://hal.archives-ouvertes.fr/hal-01403205

S. Mallat, Z. Et, and . Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, pp.3397-3415, 1993.
DOI : 10.1109/78.258082
URL : http://home.ustc.edu.cn/~zhanghan/cs/Mallat_Zhang93.pdf

D. Manolakis, R. Lockwood, T. Cooley, and J. Jacobson, « Is there a best hyperspectral detection algorithm ?, SPIE Defense, Security, and Sensing. International Society for Optics et Photonics, pp.733402-733402, 2009.
DOI : 10.1117/2.1200906.1560
URL : http://dspace.mit.edu/bitstream/1721.1/52646/1/Manolakis-2009-Is%20there%20a%20best%20hyperspectral%20detection%20algorithm.pdf

D. G. Manolakis, A. Gary, N. Shaw, and . Keshava, Comparative analysis of hyperspectral adaptive matched filter detectors, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, pp.2-17, 2000.
DOI : 10.1117/12.410332

C. Meillier, « Détection de sources quasi-ponctuelles dans des champs de données massifs, Thèse de doct. Université Grenoble Alpes (cf, p.79, 2015.

C. Meillier, F. Chatelain, O. Michel, and H. Ayasso, Nonparametric Bayesian Extraction of Object Configurations in Massive Data, IEEE Transactions on Signal Processing, vol.63, issue.8, pp.1911-1924
DOI : 10.1109/TSP.2015.2403268
URL : https://hal.archives-ouvertes.fr/hal-01129038

C. Meillier, F. Chatelain, O. Michel, R. Bacon, L. Piqueras et al., SELFI: an object-based, Bayesian method for faint emission line source detection in MUSE deep field data cubes, Astronomy & Astrophysics, vol.120, p.140, 2016.
DOI : 10.1086/316854
URL : https://hal.archives-ouvertes.fr/hal-01322356

C. Meillier, R. Bacher, F. Chatelain, and O. Michel, « Méthode de sigma-clipping par point fixe pour l'estimation de la distribution sous H0 dans le cadre de tests multiples, Colloque du Groupe de recherche et d'étude de traitement du signal et des images (GRETSI), p.2017, 2017.

A. Moffat, « A theoretical investigation of focal stellar images in the photographic emulsion and application to photographic photometry, Astronomy and Astrophysics, vol.3, issue.29, pp.455-461, 1969.

J. Nascimento, . Mp, M. Jose, and . Dias, « Does independent component analysis play a role in unmixing hyperspectral data ?, IEEE Transactions on Geoscience and Remote Sensing, vol.431, pp.175-187, 2005.

J. Nascimento, . Mp, M. José, and . Dias, Vertex component analysis: a fast algorithm to unmix hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.4, pp.898-910, 2005.
DOI : 10.1109/TGRS.2005.844293

A. Y. Ng, I. Michael, Y. Jordan, and . Weiss, « On spectral clustering : Analysis and an algorithm, Advances in neural information processing systems 2, pp.849-856, 2002.

X. Ning, I. W. Selesnick, and L. Duval, « Chromatogram baseline estimation and denoising using sparsity (BEADS) ». en, Chemometrics and Intelligent Laboratory Systems 139, pp.156-167, 2014.
DOI : 10.1016/j.chemolab.2014.09.014

S. Paris, D. Mary, and A. Ferrari, Detection Tests Using Sparse Models, With Application to Hyperspectral Data, Signal Processing, pp.1481-1494, 2013.
DOI : 10.1109/TSP.2013.2238533

O. Pony, X. Descombes, and J. Zerubia, « Classification d'images satellitaires hyperspectrales en zone rurale et périurbaine, p.148, 2000.

D. Poskitt, « Precision, complexity and Bayesian model determination, Journal of the Royal Statistical Society. Series B, pp.199-208, 1987.

M. Rafelski, I. Harry, . Teplitz, P. Jonathan, D. Gardner et al., UVUDF: ULTRAVIOLET THROUGH NEAR-INFRARED CATALOG AND PHOTOMETRIC REDSHIFTS OF GALAXIES IN THE HUBBLE ULTRA DEEP FIELD, The Astronomical Journal, vol.150, issue.1, pp.31-59, 2015.
DOI : 10.1088/0004-6256/150/1/31
URL : https://hal.archives-ouvertes.fr/hal-01439930

C. Ramirez, V. Kreinovich, and M. Argaez, « Why l1 Is a Good Approximation to l0 : A Geometric Explanation, p.44, 2013.

I. S. Reed and X. Yu, Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution, Acoustics, Speech and Signal Processing, pp.1760-1770, 1990.
DOI : 10.1109/29.60107

P. J. Rousseeuw and C. Croux, Alternatives to the Median Absolute Deviation, Journal of the American Statistical Association, vol.69, issue.424, pp.1273-1283, 1993.
DOI : 10.1093/biomet/69.1.242

P. J. Rousseeuw, M. Et-annick, and . Leroy, Robust regression and outlier detection, pp.135-137, 1987.
DOI : 10.1002/0471725382

P. J. Rousseeuw and . Et-katrien-van-driessen, Computing LTS Regression for Large Data Sets, Data Mining and Knowledge Discovery, vol.12, issue.1, pp.29-45, 2006.
DOI : 10.1007/s10618-005-0024-4

L. L. Scharf and . Mcwhorter, Adaptive matched subspace detectors and adaptive coherence estimators, Conference Record of The Thirtieth Asilomar Conference on Signals, Systems and Computers, pp.1114-1117, 1996.
DOI : 10.1109/ACSSC.1996.599116

R. A. Schowengerdt, Remote sensing : models and methods for image processing Academic press (cf, 2006.

G. Schwarz, Estimating the Dimension of a Model, The annals of statistics 6.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

P. Serra, L. Jurek, and . Flöer, Abstract, Publications of the Astronomical Society of Australia, vol.276, issue.03, pp.296-300, 2012.
DOI : 10.1111/j.1365-2966.2009.14662.x

J. Sherman, J. Winifred, and . Morrison, « Adjustment of an inverse matrix corresponding to changes in the elements of a given column or a given row of the original matrix, Annals of Mathematical Statistics. T, vol.20, issue.4, pp.621-621, 1949.

D. Slepian, The One-Sided Barrier Problem for Gaussian Noise, Bell System Technical Journal 41, pp.463-501, 1962.
DOI : 10.1002/j.1538-7305.1962.tb02419.x

J. Solomon and . Rock, « Imaging spectrometry for earth remote sensing, Science, vol.2284704, pp.1147-1152, 1985.

«. Zap-?, PCA sky subtraction for integral field spectroscopy, Monthly Notices of the Royal Astronomical Society, vol.4583, pp.3210-3220

J. D. Storey, « The positive false discovery rate : a Bayesian interpretation and the q-value ». In : The Annals of Statistics 31, pp.2013-2035, 2003.

J. D. Storey, J. E. Taylor, and D. Siegmund, « Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates : a unified approach, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.661, pp.187-205, 2004.

C. Theobald, « Generalizations of mean square error applied to ridge regression, Journal of the Royal Statistical Society. Series B, pp.103-106, 1974.

R. Tibshirani, « Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B, pp.267-288, 1996.
DOI : 10.1111/j.1467-9868.2011.00771.x

A. Tikhonov, V. Nikolaevich, F. Iakovlevich-arsenin, and . John, Solutions of ill-posed problems. T. 14, p.40, 1977.

I. Trujillo, J. Aguerri, C. Cepa, and . Gutiérrez, The effects of seeing on S??rsic profiles - II. The Moffat PSF, Monthly Notices of the Royal Astronomical Society, vol.130, issue.3, pp.977-985, 2001.
DOI : 10.1051/aas:1998219

A. W. Vaart and . Van-der, Asymptotic statistics. T. 3, p.132, 1998.

A. Villa, J. Chanussot, C. Jutten, J. A. Benediktsson, and S. Moussaoui, On the use of ICA for hyperspectral image analysis, 2009 IEEE International Geoscience and Remote Sensing Symposium, pp.97-121, 2009.
DOI : 10.1109/IGARSS.2009.5417363
URL : https://hal.archives-ouvertes.fr/hal-00449457

E. Villeneuve, « Déconvolution de données hyperspectrales pour l'instrument MUSE du VLT, Thèse de doct, 2012.

E. Villeneuve, H. Carfantan, and D. Serre, PSF estimation of hyperspectral data acquisition system for ground-based astrophysical observations, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp.1-4, 2011.
DOI : 10.1109/WHISPERS.2011.6080902

X. Wang and I. Davidson, Flexible constrained spectral clustering, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pp.563-572, 2010.
DOI : 10.1145/1835804.1835877
URL : http://www.cs.ucdavis.edu/~davidson/Publications/rp058d-wang.pdf

Q. Wei, J. Bioucas-dias, N. Dobigeon, and J. Tourneret, Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation, IEEE Transactions on Geoscience and Remote Sensing, vol.53, issue.7, pp.3658-3668, 2015.
DOI : 10.1109/TGRS.2014.2381272
URL : https://hal.archives-ouvertes.fr/hal-01168121

P. M. Weilbacher, O. Streicher, T. Urrutia, A. Jarno, A. Pécontal-rousset et al., Design and capabilities of the MUSE data reduction software and pipeline, Software and Cyberinfrastructure for Astronomy II, pp.84510-84510, 2012.
DOI : 10.1117/12.925114

M. E. Winter, « N-FINDR : an algorithm for fast autonomous spectral endmember determination in hyperspectral data, SPIE's International Symposium on Optical Science, Engineering, and Instrumentation. International Society for Optics et Photonics, pp.266-275, 1999.

Q. Xu, M. Yan, and Y. Yao, « Fast Adaptive Least Trimmed Squares for Robust Evaluation of Quality of Experience, p.135, 2014.
DOI : 10.21236/ADA610266

N. Yokoya, T. Yairi, and A. Iwasaki, Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.2, pp.528-537, 2012.
DOI : 10.1109/TGRS.2011.2161320

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, pp.49-67, 2006.
DOI : 10.1198/016214502753479356

. Zhang, S. Zhi-min, Y. Chen, and . Liang, « Baseline correction using adaptive iteratively reweighted penalized least squares ». en. In : The Analyst 135, pp.1138-135, 2010.
DOI : 10.1039/b922045c