O. Aalen, A Model for Nonparametric Regression Analysis of Counting Processes, Proc. Sixth Internat. Conf., Wiss la, pp.1-25, 1978.
DOI : 10.1007/978-1-4615-7397-5_1

P. K. Andersen, Ø. Borgan, R. D. Gill, and N. Keiding, Statistical models based on counting processes, 1993.
DOI : 10.1007/978-1-4612-4348-9

E. Bacry, S. Delattre, M. Hoffmann, and J. F. Muzy, Some limit theorems for Hawkes processes and application to financial statistics, Stochastic Processes and their Applications, vol.123, issue.7, pp.2475-2499, 2013.
DOI : 10.1016/j.spa.2013.04.007
URL : https://hal.archives-ouvertes.fr/hal-01313994

B. Bercu and A. Touati, Exponential inequalities for self-normalized martingales with applications, The Annals of Applied Probability, vol.18, issue.5, pp.1848-1869, 2008.
DOI : 10.1214/07-AAP506
URL : https://hal.archives-ouvertes.fr/hal-00165219

P. J. Bickel, Y. Ritov, and A. B. Tsybakov, Simultaneous analysis of Lasso and Dantzig selector, The Annals of Statistics, vol.37, issue.4, pp.1705-1732, 2009.
DOI : 10.1214/08-AOS620
URL : https://hal.archives-ouvertes.fr/hal-00401585

C. G. Bowsher, Stochastic kinetic models: Dynamic independence, modularity and graphs, The Annals of Statistics, vol.38, issue.4, pp.2242-2281, 2010.
DOI : 10.1214/09-AOS779
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3027064

P. Brémaud, Point processes and queues, 1981.
DOI : 10.1007/978-1-4684-9477-8

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

R. Brette and A. Destexhe, Handbook of Neural Activity Measurement, 2012.
DOI : 10.1017/CBO9780511979958
URL : https://hal.archives-ouvertes.fr/hal-00739666

E. Brunel and F. Comte, Penalized contrast estimation of density and hazard rate with censored data. Sankhy¯ a, pp.441-475, 2005.

E. Brunel and F. Comte, Adaptive Estimation of Hazard Rate with Censored Data, Communications in Statistics - Theory and Methods, vol.15, issue.8, pp.8-10, 2008.
DOI : 10.1007/s002220050108
URL : https://hal.archives-ouvertes.fr/hal-00258938

P. Bühlmann and S. Van-de-geer, Statistics for high-dimensional data, 2011.
DOI : 10.1007/978-3-642-20192-9

F. Bunea and I. W. Mckeague, Covariate selection for semiparametric hazard function regression models, Journal of Multivariate Analysis, vol.92, issue.1, pp.186-204, 2005.
DOI : 10.1016/j.jmva.2003.09.006
URL : http://doi.org/10.1016/j.jmva.2003.09.006

F. Bunea, A. B. Tsybakov, and M. H. Wegkamp, Aggregation and Sparsity Via ???1 Penalized Least Squares, Proc. 19th Annual Conf. Learning Theory (COLT 2006). Lecture Notes in Artificial Intelligence v.4005 (Lugosi, 2006.
DOI : 10.1007/11776420_29
URL : https://hal.archives-ouvertes.fr/hal-00084553

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, Aggregation for Gaussian regression, The Annals of Statistics, vol.35, issue.4, pp.1674-1697, 2007.
DOI : 10.1214/009053606000001587

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. Candes 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, vol.11, issue.456, 2010.

G. Chagny, Adaptive Warped Kernel Estimators, Scandinavian Journal of Statistics, vol.76, issue.375, 2012.
DOI : 10.1111/sjos.12109
URL : https://hal.archives-ouvertes.fr/hal-00715184

E. S. Chornoboy, L. P. Schramm, and A. F. Karr, Maximum likelihood identification of neural point process systems, Biological Cybernetics, vol.80, issue.4-5, pp.265-275, 1988.
DOI : 10.1080/01621459.1985.10477119

F. Comte, S. Ga¨?ffasga¨?ffas, 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. J. Daley, V. , and D. , An introduction to the theory of point processes. Vol. I. Probability and its Applications, 2003.

V. H. De-la-peña, A General Class of Exponential Inequalities for Martingales and Ratios, The Annals of Probability, vol.27, issue.1, pp.537-564, 1999.
DOI : 10.1214/aop/1022677271

V. H. De-la-peña, T. L. Lai, and Q. M. Shao, Self-Normalized Processes, 2009.
DOI : 10.1007/978-3-540-85636-8

K. Dzhaparidze and J. H. Van-zanten, On Bernstein-type inequalities for martingales, Stochastic Processes and their Applications, vol.93, issue.1, pp.109-117, 2001.
DOI : 10.1016/S0304-4149(00)00086-7

W. J. Fu, Penalized regressions: the bridge versus the Lasso, J. Comput . Graph. Statist, vol.7, issue.3, pp.397-416, 1998.

S. Ga¨?ffasga¨?ffas and A. Guilloux, High-dimensional additive hazards models and the Lasso, Electronic Journal of Statistics, vol.6, issue.0, pp.522-546, 2012.
DOI : 10.1214/12-EJS681

G. Grégoire, Least squares cross-validation for counting process intensities, Scand. J. Statist, vol.20, issue.4, pp.343-360, 1993.

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

. Wavelets, Approximation and Statistical Applications

A. G. Hawkes, Point spectra of some mutually exciting point processes, J. R. Stat. Soc. Ser. B, vol.33, pp.438-443, 1971.

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

M. Jacobsen, Point process theory and applications. Marked point and piecewise deterministic processes. Probability and its Applications, 2006.

V. Koltchinskii, K. Lounici, and A. B. Tsybakov, Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion, The Annals of Statistics, vol.39, issue.5, 2011.
DOI : 10.1214/11-AOS894
URL : https://hal.archives-ouvertes.fr/hal-00676868

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, 2010.
DOI : 10.3389/fncom.2010.00147

F. Letue, Modèle de Cox : Estimation par sélection de modèle et modèle de chocs bivarié, 2000.

R. Liptser and V. Spokoiny, Deviation probability bound for martingales with applications to statistical estimation, Statistics & Probability Letters, vol.46, issue.4, pp.347-357, 2000.
DOI : 10.1016/S0167-7152(99)00121-2

P. Massart, Concentration inequalities and model selection. Lectures from the 33rd Summer School on Probability Theory held in Saint-Flour, 2003.

N. Meinshausen, Relaxed Lasso, Computational Statistics & Data Analysis, vol.52, issue.1, pp.374-393, 2007.
DOI : 10.1016/j.csda.2006.12.019

M. S. Masud and R. Borisyuk, Statistical technique for analysing functional connectivity of multiple spike trains, Journal of Neuroscience Methods, vol.196, issue.1, pp.201-219, 2011.
DOI : 10.1016/j.jneumeth.2011.01.003

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, p.45101, 2010.
DOI : 10.1088/1751-8113/43/4/045101

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

V. Pernice, B. Staude, S. Cardanobile, and S. Rotter, Recurrent interactions in spiking networks with arbitrary topology, Physical Review E, vol.85, issue.3, p.31916, 2012.
DOI : 10.1103/PhysRevE.85.031916

J. W. Pillow, J. Shlens, L. Paninski, A. Sher, A. M. Litke et al., Spatio-temporal correlations and visual signalling in a complete neuronal population, Nature, vol.22, issue.7207, pp.995-999, 2008.
DOI : 10.1038/nature07140

P. Reynaud-bouret, Adaptive estimation of the intensity of inhomogeneous Poisson processes via concentration inequalities, Probability Theory and Related Fields, vol.126, issue.1, pp.103-153, 2003.
DOI : 10.1007/s00440-003-0259-1

P. Reynaud-bouret, Penalized projection estimators of the Aalen multiplicative intensity, Bernoulli, vol.12, issue.4, pp.633-661, 2006.
DOI : 10.3150/bj/1155735930

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 and E. Roy, Some non asymptotic tail estimates for Hawkes processes, Bull. Belg. Math. Soc, vol.13, issue.5, pp.883-896, 2007.

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

P. Reynaud-bouret, C. Tuleau-malot, V. Rivoirard, and F. Grammont, Spike trains as (in)homogeneous Poisson processes or Hawkes processes: nonparametric adaptive estimation and goodness-of-fit tests, 2013.

M. Rudelson and R. Vershynin, On sparse reconstruction from Fourier and Gaussian measurements, Communications on Pure and Applied Mathematics, vol.52, issue.8, pp.61-1025, 2008.
DOI : 10.1002/cpa.20227
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.111.4798

M. Rudelson and R. Vershynin, Smallest singular value of a random rectangular matrix, Communications on Pure and Applied Mathematics, vol.105, issue.2, pp.1707-1739, 2009.
DOI : 10.1002/cpa.20294

M. Rudelson and R. Vershynin, Non-asymptotic Theory of Random Matrices: Extreme Singular Values, Proceedings of the International Congress of Mathematicians 2010 (ICM 2010), pp.1576-1602, 2010.
DOI : 10.1142/9789814324359_0111

G. R. Shorack and J. A. Wellner, Empirical processes with applications to statistics, Wiley Series in Probability and Mathematical Statistics: Probability and Mathematical Statistics, 1986.
DOI : 10.1137/1.9780898719017

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

S. Van-de-geer, Exponential Inequalities for Martingales, with Application to Maximum Likelihood Estimation for Counting Processes, The Annals of Statistics, vol.23, issue.5, pp.1779-1801, 1995.
DOI : 10.1214/aos/1176324323

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

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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.119.2643

H. Zou, The Adaptive Lasso and Its Oracle Properties, Journal of the American Statistical Association, vol.101, issue.476, pp.1418-1429, 2006.
DOI : 10.1198/016214506000000735
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.649.404