Free Access
Volume 17, 2013
Page(s) 740 - 766
Published online 04 November 2013
  1. P. Barbillon, G. Celeux, A. Grimaud, Y. Lefebvre, and E. De Rocquigny, Nonlinear methods for inverse statistical problems. Comput. Stat. Data Anal. 55 (2011) 132–142. [CrossRef] [Google Scholar]
  2. P. Billingsley, Convergence of probability measures. Wiley New York (1968). [Google Scholar]
  3. Rocquigny, N. Devictor and S. Tarantola, editors. Uncertainty in industrial practice. John Wiley. [Google Scholar]
  4. M.D. Donsker, Justification and extension of Doob’s heuristic approach to the Kolmogorov-Smirnov theorems. Annal. Math. Stat. (1952) 277–281. [Google Scholar]
  5. R.M. Dudley, Weak convergence of measures on nonseparable metric spaces and empirical measures on euclidian spaces. Illinois J. Math. 11 (1966) 109–126. [Google Scholar]
  6. P. Gaenssler, Empirical Processes. Instit. Math. Stat., Hayward, CA (1983). [Google Scholar]
  7. A. Goldenshluger and O. Lepski, Uniform bounds for norms of sums of independent random functions (2009) Preprint: arXiv:0904.1950. [Google Scholar]
  8. P.J. Huber, Robust estimation of a location parameter. Annal. Math. Stat. (1964) 73–101. [Google Scholar]
  9. P.J. Huber, Robust statistics. Wiley-Interscience (1981). [Google Scholar]
  10. J.P.C. Kleijnen, Design and analysis of simulation experiments. Springer Verlag (2007). [Google Scholar]
  11. T. Klein and E. Rio, Concentration around the mean for maxima of empirical processes. Ann. Prob. 33 (2005) 1060–1077. [Google Scholar]
  12. M.R. Kosorok, Introduction to empirical processes and semiparametric inference. Springer Series in Statistics (2008). [Google Scholar]
  13. M. Ledoux, The concentration of measure phenomenon. AMS (2001). [Google Scholar]
  14. P. Massart, Concentration inequalities and model selection: Ecole d’Eté de Probabilités de Saint-Flour XXXIII-2003. Springer Verlag (2007). [Google Scholar]
  15. P. Massart and É. Nédélec, Risk bounds for statistical learning. Annal. Stat. 34 (2006) 2326–2366. [CrossRef] [MathSciNet] [Google Scholar]
  16. D. Pollard, Empirical processes: theory and applications. Regional Conference Series in Probability and Statistics Hayward (1990). [Google Scholar]
  17. N. Rachdi, J.C. Fort and T. Klein, Stochastic inverse problem with noisy simulator- an application to aeronautic model. Annal. Facult. Sci. Toulouse 21. [Google Scholar]
  18. T.J. Santner, B.J. Williams and W. Notz, The design and analysis of computer experiments. Springer Verlag (2003). [Google Scholar]
  19. G.R Shorack and J.A Wellner. Empirical processes with applications to statistics. Wiley Series in Probability and Statistics (1986). [Google Scholar]
  20. C. Soize and R. Ghanem, Physical systems with random uncertainties: chaos representations with arbitrary probability measure. SIAM J. Sci. Comput. 26 (2004) 395–410. [Google Scholar]
  21. M. Talagrand, Sharper bounds for Gaussian and empirical processes. Annal. Prob. 22 (1994) 28–76. [CrossRef] [MathSciNet] [Google Scholar]
  22. S. van de Geer, Empirical processes in M-estimation. Cambridge University Press (2000). [Google Scholar]
  23. A.W. van der Vaart, Asymptotic statistics. Cambridge University Press (2000). [Google Scholar]
  24. A.W. van der Vaart and J.A. Wellner, Weak Convergence and Empirical Processes. Springer Series in Statistics (1996). [Google Scholar]
  25. E. Vazquez. Modélisation comportementale de systèmes non-linéaires multivariables par méthodes à noyaux et applications. Ph.D. thesis (2005). [Google Scholar]

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