.. Estimation-procedure, 49 3.2.1 Methodology 49 3.2.2 Results: oracle inequality and minimax rate, p.52

A. Chapitre, Programs for i=n1+1:n2 bl=x(2,i-1)-(n-1), p.2

. Sm=2^, Wm*S; y(j0-j)=abs(Sm)

*. Snew=wf and . S=snew, Warning! The coefficients are not divided by n here endfunction The function QuantileT consists in the computation of u [U 1 ,...,Un;m] ? defined by (4.5), obtained by dichotomie. This function needs the following preliminary function testprobathresh

P. K. Bibliographie1-]-andersen, Ø. Borgan, R. D. Gill, and N. Keiding, Statistical models based on counting processes, 1993.

E. Bacry, S. Delattre, M. Hoffmann, and J. Muzy, Modelling microstructure noise with mutually exciting point processes, Quantitative Finance, vol.472, issue.7, pp.65-77, 2013.
DOI : 10.1080/14697688.2011.647054
URL : https://hal.archives-ouvertes.fr/hal-01313995

L. J. Bain, M. Engelhardt, and F. Wright, Tests for an Increasing Trend in the Intensity of a Poisson Process: A Power Study, Journal of the American Statistical Association, vol.4, issue.390, pp.419-422, 1985.
DOI : 10.1080/01621459.1985.10478133

Y. Baraud, Non asymptotic minimax rates of testing in signal detection, Bernoulli, vol.8, pp.577-606, 2002.

Y. Baraud, S. Huet, and B. Laurent, Adaptive tests of linear hypotheses by model selection, Ann. Statist, vol.31, issue.1, pp.225-251, 2003.

H. B. Barlow, Single Units and Sensation: A Neuron Doctrine for Perceptual Psychology?, Perception, vol.1, issue.2, pp.371-394, 1972.
DOI : 10.1068/p010371

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

Y. Benjamini and Y. Hochberg, Controlling the False Discovery Rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.57, issue.1, pp.289-300, 1995.

K. Bertin, L. Pennec, E. Rivoirard, and V. , Adaptive Dantzig density estimation, Annales de l'Institut Henri Poincar??, Probabilit??s et Statistiques, vol.47, issue.1
DOI : 10.1214/09-AIHP351
URL : https://hal.archives-ouvertes.fr/hal-00381984

. Ann and . Inst, Henri Poincaré Probab, Stat, vol.47, issue.1, pp.43-74, 2011.

M. Bhattacharjee, J. V. Deshpande, and U. Naik-nimbalkar, Unconditional Tests of Goodness of Fit for the Intensity of Time-Truncated Nonhomogeneous Poisson Processes, Technometrics, vol.46, issue.3, pp.330-338, 2004.
DOI : 10.1198/004017004000000356

L. Birgé and P. Massart, From Model Selection to Adaptive Estimation, pp.55-87, 1997.
DOI : 10.1007/978-1-4612-1880-7_4

S. Boucheron, O. Bousquet, G. Lugosi, and P. Massart, Moment inequalities for functions of independent random variables, The Annals of Probability, vol.33, issue.2, pp.514-560, 2005.
DOI : 10.1214/009117904000000856
URL : https://hal.archives-ouvertes.fr/hal-00101850

P. Brémaud and L. Massoulié, Stability of nonlinear Hawkes processes, Ann. Probab, vol.24, issue.3, pp.1563-1588, 1996.

P. Brémaud and L. Massoulié, Hawkes branching point processes without ancestors, Journal of Applied Probability, vol.38, issue.01, pp.122-135, 2001.
DOI : 10.1093/biomet/58.1.83

F. Bunea, Consistent selection via the Lasso for high dimensional approximating regression models, Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh, pp.122-138, 2008.
DOI : 10.1214/074921708000000101

F. Bunea, A. B. Tsybakov, and M. H. Wegkamp, Sparse Density Estimation with ???1 Penalties, Lecture Notes in Artificial Intelligence, vol.4539, pp.530-543, 2007.
DOI : 10.1007/978-3-540-72927-3_38
URL : https://hal.archives-ouvertes.fr/hal-00160850

F. Bunea, A. B. Tsybakov, and M. H. Wegkamp, Sparsity oracle inequalities for the Lasso, Electronic Journal of Statistics, vol.1, issue.0, pp.169-194, 2007.
DOI : 10.1214/07-EJS008
URL : https://hal.archives-ouvertes.fr/hal-00160646

E. J. Candès and Y. Plan, A Probabilistic and RIPless Theory of Compressed Sensing, IEEE Transactions on Information Theory, vol.57, issue.11, pp.7235-7254, 2011.
DOI : 10.1109/TIT.2011.2161794

E. J. Candès and T. Tao, Decoding by Linear Programming, IEEE Transactions on Information Theory, vol.51, issue.12, pp.514203-4215, 2005.
DOI : 10.1109/TIT.2005.858979

E. J. Candès and T. Tao, The Dantzig selector: Statistical estimation when p is much larger than n, The Annals of Statistics, vol.35, issue.6, pp.2313-2351, 2007.
DOI : 10.1214/009053606000001523

L. Carstensen, A. Sandelin, O. Winther, and N. R. Hansen, Multivariate Hawkes process models of the occurrence of regulatory elements and an analysis of the pilot ENCODE regions, BMC Bioinformatics, issue.456, p.11, 2010.

L. Cavalier and J. Koo, Poisson intensity estimation for tomographic data using a wavelet shrinkage approach, IEEE Transactions on Information Theory, vol.48, issue.10, pp.2794-2802, 2002.
DOI : 10.1109/TIT.2002.802632

A. Cohen, I. Daubechies, and J. Feauveau, Biorthogonal bases of compactly supported wavelets, Communications on Pure and Applied Mathematics, vol.10, issue.5, pp.485-560, 1992.
DOI : 10.1002/cpa.3160450502

A. Cohen, R. A. Devore, and R. Hochmuth, Restricted Nonlinear Approximation, Constructive Approximation, vol.16, issue.1, pp.85-113, 2000.
DOI : 10.1007/s003659910004
URL : http://bigcheese.math.sc.edu/~devore/publications/newrestricted.ps

A. Cohen and H. B. Sackrowitz, Evaluating Tests for Increasing Intensity of a Poisson Process, Technometrics, vol.35, issue.4, pp.446-448, 1993.
DOI : 10.2307/1270277

F. Comte, S. Gaïffas, and A. Guilloux, Adaptive estimation of the conditional intensity of marker-dependent counting processes, Annales de l'Institut Henri Poincar??, Probabilit??s et Statistiques, vol.47, issue.4, pp.1171-1196, 2011.
DOI : 10.1214/10-AIHP386
URL : https://hal.archives-ouvertes.fr/hal-00333356

D. R. Cox, Some statistical methods connected with series of events (with Discussion)

S. Dachian and Y. A. Kutoyants, Hypotheses Testing: Poisson Versus Self-exciting, Scandinavian Journal of Statistics, vol.9, issue.2, pp.391-408, 2006.
DOI : 10.2307/1426698
URL : https://hal.archives-ouvertes.fr/hal-00469707

D. J. Daley, V. , and D. , An Introduction to the Theory of Point Processes - Volume I: Elementary Theory and Methods, Probab. Appl, 2003.

D. A. Darling, The Kolmogorov-Smirnov, Cramer-von Mises Tests, The Annals of Mathematical Statistics, vol.28, issue.4, pp.823-838, 1957.
DOI : 10.1214/aoms/1177706788

I. Daubechies, Ten Lectures on Wavelets, CBMS-NSF Regional Conf. Ser. in Appl. Math, 1992.

V. H. De-la-peña and E. Giné, Decoupling: From Dependence to Independence, Probab. Appl, 1999.
DOI : 10.1007/978-1-4612-0537-1

V. H. De-la-peña and S. J. Montgomery-smith, Decoupling Inequalities for the Tail Probabilities of Multivariate $U$-Statistics, The Annals of Probability, vol.23, issue.2, pp.806-816, 1995.
DOI : 10.1214/aop/1176988291

R. A. Devore and G. G. Lorentz, Constructive Approximation, 1993.

D. L. Donoho, Nonlinear wavelet methods for recovery of signals, densities, and spectra from indirect and noisy data, Proceedings of Symposia in Applied Mathematics, pp.173-205, 1993.
DOI : 10.1090/psapm/047/1268002

D. L. Donoho, Compressed sensing, IEEE Transactions on Information Theory, vol.52, issue.4, pp.1289-1306, 2006.
DOI : 10.1109/TIT.2006.871582
URL : https://hal.archives-ouvertes.fr/inria-00369486

D. L. Donoho and I. M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Biometrika, vol.81, issue.3, pp.425-455, 1994.
DOI : 10.1093/biomet/81.3.425

D. L. Donoho and I. M. Johnstone, Adapting to Unknown Smoothness via Wavelet Shrinkage, Journal of the American Statistical Association, vol.31, issue.432, pp.1200-1224, 1995.
DOI : 10.1080/01621459.1979.10481038

D. L. Donoho and I. M. Johnstone, Neo-Classical Minimax Problems, Thresholding and Adaptive Function Estimation, Bernoulli, vol.2, issue.1, pp.39-62, 1996.
DOI : 10.2307/3318568

D. L. Donoho, I. M. Johnstone, G. Kerkyacharian, and D. Picard, Wavelet shrinkage: asymptopia? (with discussion), J. R. Stat. Soc. Ser. B, vol.57, pp.301-369, 1995.

D. L. Donoho, I. M. Johnstone, G. Kerkyacharian, and D. Picard, Density estimation by wavelet thresholding, The Annals of Statistics, vol.24, issue.2, pp.508-539, 1996.
DOI : 10.1214/aos/1032894451

D. L. Donoho, I. M. Johnstone, G. Kerkyacharian, and D. Picard, Universal Near Minimaxity of Wavelet Shrinkage, Festschrift for Lucien Le Cam, pp.183-218, 1997.
DOI : 10.1007/978-1-4612-1880-7_12

K. Fazli, Second order efficient test for inhomogeneous Poisson processes, Stat. Inference Stoch. Process, vol.2, pp.181-208, 2007.

K. Fazli and Y. A. Kutoyants, Two simple hypotheses testing for Poisson process, Far East J. Theor. Stat, vol.15, issue.2, pp.251-290, 2005.

M. Fromont, B. Laurent, and P. Reynaud-bouret, Adaptive tests of homogeneity for a Poisson process, Annales de l'Institut Henri Poincar??, Probabilit??s et Statistiques, vol.47, issue.1, pp.176-213, 2011.
DOI : 10.1214/10-AIHP367
URL : https://hal.archives-ouvertes.fr/hal-00867074

M. Fromont, B. Laurent, and P. Reynaud-bouret, The two-sample problem for Poisson processes: Adaptive tests with a nonasymptotic wild bootstrap approach, The Annals of Statistics, vol.41, issue.3, 2012.
DOI : 10.1214/13-AOS1114SUPP
URL : https://hal.archives-ouvertes.fr/hal-00679102

G. L. Gerstein, Searching for significance in spatio-temporal firing patterns, Acta Neurobiol Exp (Wars), vol.64, issue.2, pp.203-207, 2004.

E. Giné, R. Latala, and J. Zinn, Exponential and Moment Inequalities for U-Statistics, High Dimensional Probability II, pp.13-38, 2000.
DOI : 10.1007/978-1-4612-1358-1_2

A. Goldenshluger and O. Lepski, Structural adaptation via L p -norm oracle inequalities . Probab. Theory Related Fields, pp.41-71, 2009.

F. Grammont and A. Riehle, Precise spike synchronization in monkey motor cortex involved in preparation for movement, Experimental Brain Research, vol.128, issue.1-2, pp.118-122, 1999.
DOI : 10.1007/s002210050826

S. Grün, Unitary Joint-Events in Multiple-Neuron Spiking Activity: Detection, Significance , and Interpretation. Reihe Physik, Band 60, 1996.

S. Grün, M. Diesmann, and A. Aertsen, Unitary Event analysis, Analysis of Parallel Spike Trains, pp.191-220, 2010.

S. Grün, M. Diesmann, F. Grammont, A. Riehle, and A. Aertsen, Detecting unitary events without discretization of time, Journal of Neuroscience Methods, vol.94, issue.1, pp.67-79, 1999.
DOI : 10.1016/S0165-0270(99)00126-0

G. Gusto and S. Schbath, FADO: A Statistical Method to Detect Favored or Avoided Distances between Occurrences of Motifs using the Hawkes' Model, Statistical Applications in Genetics and Molecular Biology, vol.4, issue.1, 2005.
DOI : 10.2202/1544-6115.1119

D. Halpern, H. Chiapello, S. Schbath, S. Robin, C. Hennequet-antier et al., Identification of DNA Motifs Implicated in Maintenance of Bacterial Core Genomes by Predictive Modeling, PLoS Genetics, vol.95, issue.9, 2007.
DOI : 10.1371/journal.pgen.0030153.st005
URL : https://hal.archives-ouvertes.fr/hal-01197542

N. R. Hansen, P. Reynaud-bouret, and V. Rivoirard, Lasso and probabilistic inequalities for multivariate point processes, Bernoulli, vol.21, issue.1, 2012.
DOI : 10.3150/13-BEJ562
URL : https://hal.archives-ouvertes.fr/hal-00722668

W. Härdle, G. Kerkyacharian, D. Picard, A. Tsybakov, and . Wavelets, Approximation and Statistical Applications, Lecture Notes in Statist, vol.129, 1998.

Z. Harmany, R. Marcia, and R. Willett, Spatio-temporal compressed sensing with Coded Apertures and Keyed Exposures, 2011.

A. G. Hawkes, Spectra of some self-exciting and mutually exciting point processes, Biometrika, vol.58, issue.1, pp.83-90, 1971.
DOI : 10.1093/biomet/58.1.83

W. Hoeffding, A Class of Statistics with Asymptotically Normal Distribution, The Annals of Mathematical Statistics, vol.19, issue.3, pp.293-325, 1948.
DOI : 10.1214/aoms/1177730196

C. Houdré and P. Reynaud-bouret, Exponential Inequalities, with Constants, for U-statistics of Order Two, Stochastic Inequalities and Applications, pp.55-69, 2003.
DOI : 10.1007/978-3-0348-8069-5_5

J. Huang, S. Ma, and C. Zhang, Adaptive Lasso for sparse high-dimensional regression models, Statist. Sinica, vol.18, pp.1603-1618, 2008.

Y. I. Ingster and Y. A. Kutoyants, Nonparametric hypothesis testing for intensity of the poisson process, Mathematical Methods of Statistics, vol.16, issue.3, pp.217-245, 2007.
DOI : 10.3103/S1066530707030039

I. M. Johnstone, Minimax Bayes, Asymptotic Minimax and Sparse Wavelet Priors, Statistical Decision Theory and Related Topics, pp.303-326, 1994.
DOI : 10.1007/978-1-4612-2618-5_23

A. Juditsky and S. Lambert-lacroix, On minimax density estimation on \mathbb{R}}, Bernoulli, vol.10, issue.2, pp.187-220, 2004.
DOI : 10.3150/bj/1082380217

G. Kerkyacharian and D. Picard, Thresholding algorithms, maxisets and well-concentrated bases, Test, vol.102, issue.2, pp.283-345, 2000.
DOI : 10.1007/BF02595738

E. D. Kolaczyk, Wavelet shrinkage estimation of certain Poisson intensity signals using corrected thresholds, Statist. Sinica, vol.9, pp.119-135, 1999.

M. Krumin, I. Reutsky, and S. Shoham, Correlation-Based Analysis and Generation of Multiple Spike Trains Using Hawkes Models with an Exogenous Input, Frontiers in Computational Neuroscience, vol.4, issue.147, 2010.
DOI : 10.3389/fncom.2010.00147

O. Lepski, Asymptotically Minimax Adaptive Estimation. I: Upper Bounds. Optimally Adaptive Estimates, Theory of Probability & Its Applications, vol.36, issue.4, pp.682-697, 1991.
DOI : 10.1137/1136085

O. Lepski, Asymptotic minimax adaptive estimation. 2. Statistical models without optimal adaptation Adaptive estimators. Theory Probab, Appl, vol.37, pp.433-448, 1992.

O. V. Lepski, On a Problem of Adaptive Estimation in Gaussian White Noise, Topics in Nonparametric Estimation, pp.87-106, 1992.
DOI : 10.1137/1135065

R. Lestienne, Determination of the precision of spike timing in the visual cortex of anaesthetised cats, Biological Cybernetics, vol.17, issue.1, pp.55-61, 1996.
DOI : 10.1007/BF00199137

S. Mallat, Multiresolution Approximations and Wavelet Orthonormal Bases of L 2 (R), Transactions of the American Mathematical Society, vol.315, issue.1
DOI : 10.2307/2001373

S. Mallat, A Wavelet Tour of Signal Processing, 2008.

R. Marcia and R. Willett, Compressive coded aperture superresolution image reconstruction, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008.
DOI : 10.1109/ICASSP.2008.4517739

P. Massart, Concentration Inequalities and Model Selection -Éc. Été Probab, Lecture Notes in Math, vol.1896, 2003.

Y. Meyer, Wavelets and Operators, 1995.

L. Mitchell and M. E. Cates, Hawkes process as a model of social interactions: a view on video dynamics, Journal of Physics A: Mathematical and Theoretical, vol.43, issue.4, 2010.
DOI : 10.1088/1751-8113/43/4/045101

J. Møller, A. Syversveen, and R. P. Waagepetersen, Log Gaussian Cox processes. Scand, J. Stat, vol.25, issue.3, pp.451-482, 1998.

G. P. Nason, Wavelet shrinkage using cross-validation, J. R. Stat. Soc. Ser. B, vol.58, pp.463-479, 1996.

Y. Ogata and H. Akaike, On Linear Intensity Models for Mixed Doubly Stochastic Poisson and Self-exciting Point Processes, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.44, issue.1, pp.102-107, 1982.
DOI : 10.1007/978-1-4612-1694-0_20

T. Ozaki, Maximum likelihood estimation of Hawkes' self-exciting point processes, Annals of the Institute of Statistical Mathematics, vol.18, issue.1, pp.145-155, 1979.
DOI : 10.1007/BF02480272

V. Pernice, B. Staude, S. Cardanobile, and S. Rotter, How Structure Determines Correlations in Neuronal Networks, PLoS Computational Biology, vol.52, issue.5, 2011.
DOI : 10.1371/journal.pcbi.1002059.s001

V. Pernice, B. Staude, S. Cardanobile, and S. Rotter, Recurrent interactions in spiking networks with arbitrary topoplogy, Phys. Rev. E, vol.85, issue.3, p.2012

F. Picard, S. Schbath, E. Lebarbier, P. Neuvial, and J. Et-chiquet, Statistiques et génome, Gazette des Mathématiciens, vol.130, pp.51-82, 2011.

J. G. Rasmussen, Aspects of temporal and spatio-temporal processes, 2006.

G. Reinert and S. Schbath, Large compound Poisson approximations for occurrences of multiple words, In Statistics in Molecular Biology and Genetics IMS Lecture Notes Monogr. Ser, vol.33, pp.257-275, 1999.
DOI : 10.1214/lnms/1215455557

G. Reinert, S. Schbath, and M. S. Waterman, Statistics on words with applications to biological sequences, Applied Combinatorics on Words, 2005.

P. Reynaud-bouret, Adaptive estimation of the intensity of inhomogeneous Poisson processes via concentration inequalities. Probab. Theory Related Fields, pp.103-153, 2003.

P. Reynaud-bouret and V. Rivoirard, Near optimal thresholding estimation of a Poisson intensity on the real line, Electronic Journal of Statistics, vol.4, issue.0, pp.172-238, 2010.
DOI : 10.1214/08-EJS319
URL : https://hal.archives-ouvertes.fr/hal-00634406

P. Reynaud-bouret, V. Rivoirard, and C. Tuleau-malot, Adaptive density estimation: A curse of support?, Journal of Statistical Planning and Inference, vol.141, issue.1, pp.115-139, 2011.
DOI : 10.1016/j.jspi.2010.05.017
URL : https://hal.archives-ouvertes.fr/hal-00634421

P. Reynaud-bouret and S. Schbath, Adaptive estimation for Hawkes processes; application to genome analysis, The Annals of Statistics, vol.38, issue.5, pp.2781-2822, 2010.
DOI : 10.1214/10-AOS806
URL : https://hal.archives-ouvertes.fr/hal-00863958

V. Rivoirard, Maxisets for linear procedures, Statistics & Probability Letters, vol.67, issue.3, pp.267-275, 2004.
DOI : 10.1016/j.spl.2003.12.010
URL : https://hal.archives-ouvertes.fr/hal-00634272

V. Rivoirard, Thresholding procedure with priors based on Pareto distributions, Test, vol.93, issue.441, pp.213-246, 2004.
DOI : 10.1007/BF02603007
URL : https://hal.archives-ouvertes.fr/hal-00634277

J. Romberg, Compressive Sensing by Random Convolution, SIAM Journal on Imaging Sciences, vol.2, issue.4, pp.1098-1128, 2009.
DOI : 10.1137/08072975X
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.329.5393

M. Rudelson and R. Vershynin, On sparse reconstruction from Fourier and Gaussian measurements, Communications on Pure and Applied Mathematics, vol.52, issue.8, pp.1025-1045, 2008.
DOI : 10.1002/cpa.20227

M. Rudemo, Empirical choice of histograms and kernel density estimators. Scand, J. Stat, vol.9, pp.65-78, 1982.

K. S. Sandhu, G. Li, H. M. Poh, Y. L. Quek, Y. Y. Sia et al., Large-Scale Functional Organization of Long-Range Chromatin Interaction Networks, Cell Reports, vol.2, issue.5, pp.1207-1219, 2012.
DOI : 10.1016/j.celrep.2012.09.022

S. Schbath, Compound Poisson approximation of word counts in DNA sequences, ESAIM: Probability and Statistics, vol.1, pp.1-16, 1995.
DOI : 10.1051/ps:1997100

S. Schbath and S. Robin, How Can Pattern Statistics Be Useful for DNA Motif Discovery?, Scan Statistics: Methods and Applications (Statistics for Industry and Technology)
DOI : 10.1007/978-0-8176-4749-0_15
URL : https://hal.archives-ouvertes.fr/hal-01197508

U. Tanaka, Y. Ogata, and D. Stoyan, Parameter Estimation and Model Selection for Neyman-Scott Point Processes, Biometrical Journal, vol.36, issue.1, pp.43-57, 2008.
DOI : 10.1002/bimj.200610339

R. Tibshirani, Regression shrinkage and selection via the Lasso, J. R. Stat. Soc. Ser. B Stat. Methodol, vol.58, issue.1, pp.267-288, 1996.

F. Touzain, M. Petit, S. Schbath, E. Karoui, and M. , DNA motifs that sculpt the bacterial chromosome, Nature Reviews Microbiology, vol.15, issue.1, pp.15-26, 2011.
DOI : 10.1038/nrmicro2477
URL : https://hal.archives-ouvertes.fr/hal-01019344

H. Triebel, Theory of Function Spaces, Monogr. Math. Birkhäuser Verlag, vol.78, 1983.

A. B. Tsybakov, Introduction à l'estimation non-paramétrique, Mathématiques & Applications, vol.41, 2004.

C. Tuleau-malot, A. Rouis, P. Reynaud-bouret, and F. Grammont, Multiple tests based on a Gaussian approximation of the Unitary Events method, p.757323, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00757323

S. Van-de-geer, High-dimensional generalized linear models and the lasso, The Annals of Statistics, vol.36, issue.2, pp.614-645, 2008.
DOI : 10.1214/009053607000000929

S. Van-de-geer, P. Bühlmann, and S. Zhou, The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso), Electronic Journal of Statistics, vol.5, issue.0, pp.688-749, 2011.
DOI : 10.1214/11-EJS624

A. W. Van-der-vaart, Asymptotic Statistics, volume 3 of Camb, Ser. Stat. Probab. Math, 1998.

D. Vere-jones and T. Ozaki, Some examples of statistical estimation applied to earthquake data, Annals of the Institute of Statistical Mathematics, vol.26, issue.3, pp.189-207, 1982.
DOI : 10.1007/BF02481022

R. M. Willett and R. D. Nowak, Multiscale Poisson Intensity and Density Estimation, IEEE Transactions on Information Theory, vol.53, issue.9, pp.3171-3187, 2007.
DOI : 10.1109/TIT.2007.903139

Y. Ying and C. Campbell, Rademacher Chaos Complexities for Learning the Kernel Problem, Neural Computation, vol.8, issue.11, pp.2858-2886, 2010.
DOI : 10.1214/aos/1079120130

B. Zhang, J. M. Fadili, and J. Starck, Wavelets, Ridgelets, and Curvelets for Poisson Noise Removal, IEEE Transactions on Image Processing, vol.17, issue.7, pp.1093-1108, 2008.
DOI : 10.1109/TIP.2008.924386
URL : https://hal.archives-ouvertes.fr/hal-00259509

H. Zou, The Adaptive Lasso and Its Oracle Properties, Journal of the American Statistical Association, vol.101, issue.476, 2006.
DOI : 10.1198/016214506000000735