R. J. Adler and R. Pyke, Uniform quadratic variation for Gaussian processes, Stochastic Process Appl, vol.48, issue.2, pp.90044-90049, 1993.

J. M. Azaïs and M. Wschebor, Level sets and extrema of random processes and elds, 2009.

F. Bachoc, Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes, Journal of Multivariate Analysis, vol.125, p.135, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00906934

J. M. Bates and C. W. Granger, The combination of forecasts, Journal of the Operational Research Society, vol.20, issue.4, p.451468, 1969.

G. Baxter, A strong limit theorem for Gaussian processes, Proc Amer Math Soc, vol.7, p.522527, 1956.

J. F. Coeurjolly, Estimating the parameters of a fractional Brownian motion by discrete variations of its sample paths, Stat Inference Stoch Process, vol.4, issue.2, p.199227, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00383118

S. Cohen and J. Istas, Fractional elds and applications, Mathématiques & Applications (Berlin), vol.73, 2013.

R. Dahlhaus, Ecient parameter estimation for self-similar processes. The annals of Statistics pp 17491766, 1989.

I. Daubechies, Orthonormal bases of compactly supported wavelets, Communications on pure and applied mathematics, vol.41, issue.7, p.909996, 1988.

, Semi-parametric estimation of the variogram of a Gaussian process with stationary increments 21

E. G. Glady²ev, A new limit theorem for stochastic processes with Gaussian increments, Teor Verojatnost i Primenen, vol.6, p.5766, 1961.

U. Grenander, Abstract inference, wiley Series in Probability and Mathematical Statistics, 1981.

X. Guyon and J. León, Convergence en loi des H-variations d'un processus gaussien stationnaire sur R, Ann Inst H Poincaré Probab Statist, vol.25, issue.3, p.265282, 1989.

J. Istas and G. Lang, Quadratic variations and estimation of the local Hölder index of a Gaussian process, Ann Inst H Poincaré Probab Statist, vol.33, issue.4, p.407436, 1997.

J. T. Kent and A. Wood, Estimating the fractal dimension of a locally self-similar Gaussian process by using increments, J Roy Statist Soc Ser B, vol.59, issue.3, p.679699, 1997.

G. Lang and F. Roue, Semi-parametric estimation of the Hölder exponent of a stationary Gaussian process with minimax rates, Stat Inference Stoch Process, vol.4, issue.3, p.283306, 2001.

F. Lavancier and P. Rochet, A general procedure to combine estimators, Computational Statistics & Data Analysis, vol.94, p.175192, 2016.
URL : https://hal.archives-ouvertes.fr/hal-00936024

P. Lévy, Le mouvement brownien plan, Amer J Math, vol.62, p.487550, 1940.

D. Luenberger, Introduction to Dynamic Systems: Theory, Models, and Applications, 1979.

O. Perrin, Quadratic variation for Gaussian processes and application to time deformation, Stochastic Process Appl, vol.82, issue.2, p.293305, 1999.
DOI : 10.1016/s0304-4149(99)00037-x

URL : https://doi.org/10.1016/s0304-4149(99)00037-x

G. Pólya, Remarks on characteristic functions, Proceedings of the First Berkeley Symposium on Mathematical Statistics and Probability, vol.501, p.115123, 1945.

C. Rasmussen and C. Williams, Gaussian Processes for Machine Learning, 2006.

L. Risser, F. Vialard, R. Wolz, M. Murgasova, D. Holm et al., ADNI: Simultaneous multiscale registration using large deformation dieomorphic metric mapping, IEEE Transactions on Medical Imaging, vol.30, issue.10, p.17461759, 2011.
DOI : 10.1109/tmi.2011.2146787

O. Roustant, D. Ginsbourger, and Y. Deville, Dicekriging, diceoptim: Two r packages for the analysis of computer experiments by kriging-based metamodeling and optimization, Journal of Statistical Software, vol.51, issue.1, p.155, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00495766

O. Roustant, D. Ginsbourger, and Y. Deville, DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by Kriging-based metamodeling and optimization, Journal of Statistical Software, vol.51, issue.1, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00495766

T. J. Santner, B. J. Williams, and W. I. Notz, The design and analysis of computer experiments, Springer Series in Statistics, 2003.

D. Slepian, On the zeros of Gaussian noise, Proc. Sympos. Time Series Analysis, p.104115, 1962.

M. Stein, Interpolation of Spatial Data: Some Theory for Kriging, 1999.