F. Anstett-collin, J. Goffart, T. Mara, and L. Denis-vidal, Sensitivity analysis of complex models: Coping with dynamic and static inputs, Reliability Engineering & System Safety, vol.134, pp.268-275, 2015.
DOI : 10.1016/j.ress.2014.08.010
URL : https://hal.archives-ouvertes.fr/hal-01063795

J. Bien and R. J. Tibshirani, Sparse estimation of a covariance matrix, Biometrika, vol.98, issue.4, pp.807-820, 2011.
DOI : 10.1093/biomet/asr054

E. Bongiorno and A. Goia, Some insights about the small ball probability factorization for hilbert random elements (2015)

E. Bongiorno and A. Goia, Classification methods for Hilbert data based on surrogate density, Computational Statistics & Data Analysis, vol.99, pp.204-222, 2016.
DOI : 10.1016/j.csda.2016.01.019

E. G. Bongiorno, E. Salinelli, A. Goia, and P. Vieu, Contributions in infinite-dimensional statistics and related topics, Contributions in infinite-dimensional statistics and related topics, 2014.
DOI : 10.15651/9788874887637

E. De-rocquigny, N. Devictor, and S. Tarantola, Uncertainty in industrial practice, 2008.
DOI : 10.1002/9780470770733

A. Delaigle and P. Hall, Defining probability density for a distribution of random functions. The Annals of Statistics, pp.1171-1193, 2010.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series B (Methodological), vol.39, pp.1-38, 1977.

F. Ferraty and P. Vieu, Nonparametric functional data analysis: theory and practice, 2006.

J. Friedman, T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, pp.432-441, 2008.
DOI : 10.1093/biostatistics/kxm045

M. Fromont, B. Laurent, M. Lerasle, and P. Reynaud-bouret, Kernels based tests with non-asymptotic bootstrap approaches for two-sample problem, 25th Annual Conference on Learning Theory, pp.1-22, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00913879

R. G. Ghanem and P. D. Spanos, Stochastic finite elements: a spectral approach, 1991.
DOI : 10.1007/978-1-4612-3094-6

A. Goia and P. Vieu, An introduction to recent advances in high/infinite dimensional statistics, Journal of Multivariate Analysis, vol.146, pp.1-6, 2016.
DOI : 10.1016/j.jmva.2015.12.001

T. Hastie and R. Tibshirani, Generalized Additive Models, 1990.

J. Helton, J. Johnson, C. Sallaberry, and C. Storlie, Survey of sampling-based methods for uncertainty and sensitivity analysis, Reliability Engineering & System Safety, vol.91, issue.10-11, pp.10-11, 2006.
DOI : 10.1016/j.ress.2005.11.017

L. Horváth and P. Kokoszka, Inference for functional data with applications, 2012.
DOI : 10.1007/978-1-4614-3655-3

A. Höskuldsson, PLS regression methods, Journal of Chemometrics, vol.8, issue.3, pp.211-228, 1988.
DOI : 10.1002/cem.1180020306

R. J. Hyndman and H. L. Shang, Rainbow Plots, Bagplots, and Boxplots for Functional Data, Journal of Computational and Graphical Statistics, vol.19, issue.1, pp.29-45, 2010.
DOI : 10.1198/jcgs.2009.08158

J. Jacques and C. Preda, Functional data clustering: a survey Advances in Data Analysis and Classification, pp.231-255, 2014.

J. Jacques and C. Preda, Model-based clustering for multivariate functional data, Computational Statistics & Data Analysis, vol.71, pp.92-106, 2014.
DOI : 10.1016/j.csda.2012.12.004
URL : https://hal.archives-ouvertes.fr/hal-00943732

M. Loève, Probability theory, 1955.

X. Ma and N. Zabaras, Kernel principal component analysis for stochastic input model generation, Journal of Computational Physics, vol.230, issue.19, pp.7311-7331, 2011.
DOI : 10.1016/j.jcp.2011.05.037

A. Marrel, B. Iooss, F. Van-dorpe, and E. Volkova, An efficient methodology for modeling complex computer codes with Gaussian processes, Computational Statistics & Data Analysis, vol.52, issue.10, pp.4731-4744, 2008.
DOI : 10.1016/j.csda.2008.03.026
URL : https://hal.archives-ouvertes.fr/hal-00239492

J. Mclachlan and T. Krishnan, The EM algorithm and extension, Wiley inter-science, 1997.

J. Oakley and A. O-'hagan, Bayesian inference for the uncertainty distribution of computer model outputs, Biometrika, vol.89, issue.4, pp.769-784, 2002.
DOI : 10.1093/biomet/89.4.769

A. L. Popelin and B. Iooss, Visualization tools for uncertainty and sensitivity analyses on thermal-hydraulic transients, SNA + MC 2013, Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo, 2013.
DOI : 10.1051/snamc/201403403
URL : https://hal.archives-ouvertes.fr/hal-00952930

J. O. Ramsay and B. W. Silverman, Functional Data Analysis, 2005.

C. E. Rasmussen and C. K. Williams, Gaussian Processes in Machine Learning, 2006.
DOI : 10.1162/089976602317250933

M. Rosenblatt, Remarks on Some Nonparametric Estimates of a Density Function, The Annals of Mathematical Statistics, vol.27, issue.3, pp.832-837, 1956.
DOI : 10.1214/aoms/1177728190

J. Sacks, W. J. Welch, T. J. Mitchell, and H. P. Wynn, Design and Analysis of Computer Experiments, Statistical Science, vol.4, issue.4, pp.409-423, 1989.
DOI : 10.1214/ss/1177012413

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

D. W. Scott, Multivariate density estimation: theory, practice, and visualization, 2009.
DOI : 10.1002/9781118575574

K. Van-deun, A. Smilde, M. Van-der-werf, H. Kiers, and I. Van-mechelen, A structured overview of simultaneous component based data integration, BMC Bioinformatics, vol.10, issue.1, pp.246-261, 2009.
DOI : 10.1186/1471-2105-10-246

J. Wan and N. Zabaras, A probabilistic graphical model based stochastic input model construction, Journal of Computational Physics, vol.272, pp.664-685, 2014.
DOI : 10.1016/j.jcp.2014.05.002

H. Wang, Coordinate descent algorithm for covariance graphical lasso, Statistics and Computing, vol.2, issue.1, pp.1-9, 2013.
DOI : 10.1007/s11222-013-9385-5

W. J. Welch, R. J. Buck, J. Sacks, H. P. Wynn, T. J. Mitchell et al., Screening, Predicting, and Computer Experiments, Technometrics, vol.34, issue.1, pp.15-25, 1992.
DOI : 10.2307/1269548

H. Wold, Estimation of Principal Components and Related Models by Iterative Least squares, pp.391-420, 1966.

C. F. Wu, On the Convergence Properties of the EM Algorithm, The Annals of Statistics, vol.11, issue.1, pp.95-103, 1983.
DOI : 10.1214/aos/1176346060