M. Allen, The relationship between variable selection and data agumentation and a method for prediction, Technometrics, vol.16, issue.1, pp.125-127, 1974.

F. Bachoc, Cross validation and maximum likelihood estimation of hyper-parameters of Gaussian processes with model misspecification, Computational Statistics and Data Analysis, vol.66, pp.55-69, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00905400

C. E. Rasmussen and C. K. Williams, Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning, 2006.

O. Dubrule, Cross validation of kriging in a unique neighborhood, Journal of the International Association for Mathematical Geology, vol.15, pp.687-699, 1983.

T. Gneiting and A. Raftery, Strictly proper scoring rules, prediction, and estimation, Journal of the American Statistical Association, vol.102, pp.359-378, 2007.

S. Sundararajan and S. S. Keerthi, Predictive approaches for choosing hyperparameters in Gaussian processes, Neural Computation, vol.13, issue.5, pp.1103-1118, 2001.

P. Craven and G. Wahba, Estimating the correct degree of smoothing by the method of generalized cross-validation, Numerische Mathematik, vol.31, pp.377-404, 1979.

T. C. Hu and M. T. Shing, Computation of matrix chain products. Part I, SIAM Journal on Computing, vol.11, issue.2, pp.362-373, 1982.

J. Bect and E. Vazquez, STK: a Small (Matlab/Octave) Toolbox for Kriging. Release 2.6, 2019.

D. J. Toal, A. I. Forrester, N. W. Bressloff, A. J. Keane, and C. Holden, An adjoint for likelihood maximization, Proceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences, vol.465, 2009.

S. Linnainmaa, The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors, 1970.