P. Guttorp and A. M. Schmidt, Covariance structure of spatial and spatiotemporal processes, Wiley Interdisciplinary Reviews: Computational Statistics, vol.24, issue.4, pp.279-287, 2013.
DOI : 10.1002/wics.1259

P. D. Sampson, D. Damian, and P. Guttorp, Advances in Modeling and Inference for Environmental Processes with Nonstationary Spatial Covariance, Geoenv III -Geostatistics for Environmental Applications, vol.11, 2001.
DOI : 10.1007/978-94-010-0810-5_2

P. Guttorp and P. D. Sampson, 20 Methods for estimating heterogeneous spatial covariance functions with environmental applications, pp.661-689, 1994.
DOI : 10.1016/S0169-7161(05)80022-7

D. Higdon, J. Swall, and J. Kern, Non-stationary spatial modeling, Bayesian Statistics, vol.6, 1998.

Z. Zhu and Y. Wu, Estimation and Prediction of a Class of Convolution-Based Spatial Nonstationary Models for Large Spatial Data, Journal of Computational and Graphical Statistics, vol.19, issue.1, pp.74-95, 2010.
DOI : 10.1198/jcgs.2009.07123

C. J. Paciorek and M. J. Schervish, Spatial modelling using a new class of nonstationary covariance functions, Environmetrics, vol.99, issue.5, pp.483-506, 2006.
DOI : 10.1002/env.785

M. Stein, Nonstationary spatial covariance functions, 2005.

E. Porcu, J. Mateu, and G. Christakos, Quasi-arithmetic means of covariance functions with potential applications to space???time data, Journal of Multivariate Analysis, vol.100, issue.8, pp.1830-1844, 2009.
DOI : 10.1016/j.jmva.2009.02.013

J. Mateu, G. Fernandez-aviles, and J. Montero, On a class of non-stationary, compactly supported spatial covariance functions, Stochastic Environmental Research and Risk Assessment, vol.4, issue.8, pp.1-13, 2010.
DOI : 10.1007/s00477-011-0510-8

E. B. Anderes and M. L. Stein, Local likelihood estimation for nonstationary random fields, Journal of Multivariate Analysis, vol.102, issue.3, pp.506-520, 2011.
DOI : 10.1016/j.jmva.2010.10.010

B. Matérn, Spatial Variation, Lecture Notes in Statistics, vol.36, 1986.
DOI : 10.1007/978-1-4615-7892-5

J. P. Chiì-es and P. Delfiner, Geostatistics: modeling spatial uncertainty, 2012.

I. Schoenberg, Metric Spaces and Positive Definite Functions, p.888, 1988.

G. F. Matheron, The theory of regionalized variables and its applications, 1971.

T. C. Haas, Lognormal and Moving Window Methods of Estimating Acid Deposition, Journal of the American Statistical Association, vol.22, issue.412, pp.950-963, 1990.
DOI : 10.1002/0471725218

M. Wand and C. Jones, Kernel smoothing, Monographs on Statistics and Applied Probability, 1995.

H. Zhang and Y. Wang, Kriging and cross-validation for massive spatial data, Environmetrics, vol.23, issue.1, pp.290-304, 2010.
DOI : 10.1002/env.1023

C. Lantuejoul and N. Desassis, Simulation of a gaussian random vector: A propagative version of the gibbs sampler, The 9th International Geostatistics Congress, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00709250

A. B. Mcbratney, B. Minasny, and . Spacebender, Spacebender, Spatial Statistics, vol.4, pp.57-67, 2013.
DOI : 10.1016/j.spasta.2013.04.001

D. Allard, Validation d'un modèle géostatistique pour l'interpolation: applicationàévénementapplication`applicationàapplicationà´applicationàévénement pluvieux, Analyse statistique des données spatiales, pp.403-414, 2006.

J. H. Neto, A. M. Schmidt, and P. Guttorp, Accounting for spatially varying directional effects in spatial covariance structures, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.96, issue.1, pp.103-122, 2013.
DOI : 10.1111/rssc.12027

C. A. Risser and D. Mark, Calder, Regression-based covariance functions for nonstationary spatial modeling, 2014.

D. L. Mcleish, A robust alternative to the normal distribution, Canadian Journal of Statistics, vol.28, issue.2, pp.89-102, 1982.
DOI : 10.2307/3314901