K. Antonio and J. Beirlant, Actuarial statistics with generalized linear mixed models, Insurance: Mathematics and Economics, vol.40, issue.1, pp.58-76, 2007.
DOI : 10.1016/j.insmatheco.2006.02.013
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.67.4900

D. Belomestny, Statistical inference for time-changed L??vy processes via composite characteristic function estimation, The Annals of Statistics, vol.39, issue.4, pp.2205-2242, 2011.
DOI : 10.1214/11-AOS901

D. Belomestny and J. Schoenmakers, Statistical Skohorod embedding problem and its generalizations, 2014.

L. Birgé and P. Massart, Minimum Contrast Estimators on Sieves: Exponential Bounds and Rates of Convergence, Bernoulli, vol.4, issue.3, pp.329-375, 1998.
DOI : 10.2307/3318720

L. Birgé and P. Massart, Minimal Penalties for Gaussian Model Selection, Probability Theory and Related Fields, vol.6, issue.1-2, pp.33-73, 2007.
DOI : 10.1007/s00440-006-0011-8

C. Butucea and A. B. Tsybakov, Sharp optimality in density deconvolution with dominating bias. I. Theory Probab, Appl, vol.52, pp.24-39, 2008.

C. Butucea and A. B. Tsybakov, Sharp optimality in density deconvolution with dominating bias. II. Theory Probab, Appl, vol.52, pp.237-249, 2008.

C. Chesneau, F. Comte, and F. Navarro, Fast nonparametric estimation for convolutions of densities, Canadian Journal of Statistics, vol.51, issue.4, pp.617-636, 2013.
DOI : 10.1002/cjs.11191
URL : https://hal.archives-ouvertes.fr/hal-00798766

F. Comte, C. Duval, and V. Genon-catalot, Nonparametric density estimation in compound Poisson processes using convolution power estimators, Metrika, vol.153, issue.1, pp.163-183, 2014.
DOI : 10.1007/s00184-013-0475-3

F. Comte and . Genon-catalot, Adaptive Laguerre density estimation for mixed Poisson models, Electronic Journal of Statistics, vol.9, issue.1, 2013.
DOI : 10.1214/15-EJS1028
URL : https://hal.archives-ouvertes.fr/hal-00848158

F. Comte and . Genon-catalot, Nonparametric estimation for pure jump L??vy processes based on high frequency data, Stochastic Processes and their Applications, pp.4088-4123, 2009.
DOI : 10.1016/j.spa.2009.09.013

M. Davidian and D. M. Giltinan, Nonlinear Models for Repeated Measurement Data, 1995.

C. Duval, Density estimation for compound Poisson processes from discrete data. Stochastic Process, Appl, vol.123, pp.3963-3986, 2013.

P. Embrechts, C. Klüppelberg, and T. Mikosch, Modelling extremal events, 1997.
DOI : 10.1007/978-3-642-33483-2

J. E. Figueroa-lópez, Nonparametric estimation of Lévy models based on discrete-sampling. Optimality, IMS Lecture Notes Monogr. Ser. Inst. Math. Statist, vol.57, pp.117-146, 2009.

B. Van-es, S. Gugushvili, and P. Spreij, A kernel type nonparametric density estimator for decompounding, Bernoulli, vol.13, issue.3, p.672694, 2007.
DOI : 10.3150/07-BEJ6091

J. Grandell, Mixed Poisson processes, Monographs on Statistics and Applied Probability, 1997.
DOI : 10.1007/978-1-4899-3117-7

S. Gugushvili, Nonparametric inference for discretely sampled L??vy processes, Annales de l'Institut Henri Poincar??, Probabilit??s et Statistiques, vol.48, issue.1, pp.282-307, 2012.
DOI : 10.1214/11-AIHP433

P. Hall and C. C. Heyde, Martingale limit theory and its application. Probability and Mathematical Statistics, 1980.

T. Klein and E. Rio, Concentration around the mean for maxima of empirical processes, The Annals of Probability, vol.33, issue.3, pp.1060-1077, 2005.
DOI : 10.1214/009117905000000044

T. Mikosch, Non-life insurance mathematics. An introduction with the Poisson process, 2009.
DOI : 10.1007/978-3-540-88233-6

M. H. Neumann and M. Reiss, Nonparametric estimation for L??vy processes from low-frequency observations, Bernoulli, vol.15, issue.1, pp.223-248, 2009.
DOI : 10.3150/08-BEJ148

J. C. Pinheiro and D. M. Bates, Mixed-Effects Models in S and S-PLUS, 2000.
DOI : 10.1007/978-1-4419-0318-1

F. A. Ueltzhöfer and C. Klüppelberg, An oracle inequality for penalised projection estimation of L??vy densities from high-frequency observations, Journal of Nonparametric Statistics, vol.36, issue.4, pp.967-989, 2011.
DOI : 10.1080/0094965021000015477