N. Cressie, Statistics for spatial data, 1993.

H. Zhang and D. Zimmerman, Towards reconciling two asymptotic frameworks in spatial statistics, Biometrika, vol.92, issue.4, pp.921-936, 2005.
DOI : 10.1093/biomet/92.4.921

K. Mardia and R. Marshall, Maximum likelihood estimation of models for residual covariance in spatial regression, Biometrika, vol.71, issue.1, pp.135-146, 1984.
DOI : 10.1093/biomet/71.1.135

N. Cressie and S. Lahiri, The Asymptotic Distribution of REML Estimators, Journal of Multivariate Analysis, vol.45, issue.2, pp.217-233, 1993.
DOI : 10.1006/jmva.1993.1034

N. Cressie and S. Lahiri, Asymptotics for REML estimation of spatial covariance parameters, Journal of Statistical Planning and Inference, vol.50, issue.3, pp.327-341, 1996.
DOI : 10.1016/0378-3758(95)00061-5

B. A. Shaby and D. Ruppert, Tapered Covariance: Bayesian Estimation and Asymptotics, Journal of Computational and Graphical Statistics, vol.50, issue.2, pp.433-452, 2012.
DOI : 10.1080/00949658908811149

F. Bachoc, Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes, Journal of Multivariate Analysis, vol.125, pp.1-35, 2014.
DOI : 10.1016/j.jmva.2013.11.015
URL : https://hal.archives-ouvertes.fr/hal-00906934

M. Stein, Asymptotically Efficient Prediction of a Random Field with a Misspecified Covariance Function, The Annals of Statistics, vol.16, issue.1, pp.55-63, 1988.
DOI : 10.1214/aos/1176350690

M. Stein, Bounds on the Efficiency of Linear Predictions Using an Incorrect Covariance Function, The Annals of Statistics, vol.18, issue.3, pp.1116-1138, 1990.
DOI : 10.1214/aos/1176347742

M. Stein, Uniform Asymptotic Optimality of Linear Predictions of a Random Field Using an Incorrect Second-Order Structure, The Annals of Statistics, vol.18, issue.2, pp.850-872, 1990.
DOI : 10.1214/aos/1176347629

Z. Ying, Asymptotic properties of a maximum likelihood estimator with data from a Gaussian process, Journal of Multivariate Analysis, vol.36, issue.2, pp.280-296, 1991.
DOI : 10.1016/0047-259X(91)90062-7

Z. Ying, Maximum Likelihood Estimation of Parameters under a Spatial Sampling Scheme, The Annals of Statistics, vol.21, issue.3, pp.1567-1590, 1993.
DOI : 10.1214/aos/1176349272

A. Van and . Vaart, Maximum likelihood estimation under a spatial sampling scheme, The Annals of Statistics, vol.5, pp.2049-2057, 1996.

M. Abt and W. Welch, Fisher information and maximum-likelihood estimation of covariance parameters in Gaussian stochastic processes, Canadian Journal of Statistics, vol.44, issue.1, pp.127-137, 1998.
DOI : 10.2307/3315678

H. Chen, D. Simpson, and Z. Ying, Infill asymptotics for a stochastic process model with measurement error, Statistica Sinica, vol.10, pp.141-156, 2000.

W. Loh and T. Lam, Estimating structured correlation matrices in smooth Gaussian random fields models, The Annals of Statistics, vol.28, pp.880-904, 2000.

W. Loh, Fixed-domain asymptotics for a subclass of Mat??rn-type Gaussian random fields, The Annals of Statistics, vol.33, issue.5, pp.2344-2394, 2005.
DOI : 10.1214/009053605000000516

E. Anderes, On the consistent separation of scale and variance for Gaussian random fields, The Annals of Statistics, vol.38, issue.2, pp.870-893, 2010.
DOI : 10.1214/09-AOS725

H. Zhang, Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics, Journal of the American Statistical Association, vol.99, issue.465, pp.250-261, 2004.
DOI : 10.1198/016214504000000241

C. Kaufman and B. Shaby, The role of the range parameter for estimation and prediction in geostatistics, Biometrika, vol.100, issue.2, pp.473-484, 2013.
DOI : 10.1093/biomet/ass079

J. Du, H. Zhang, and V. Mandrekar, Fixed-domain asymptotic properties of tapered maximum likelihood estimators, The Annals of Statistics, vol.37, issue.6A, pp.3330-3361, 2009.
DOI : 10.1214/08-AOS676

D. Wang and W. Loh, On fixed-domain asymptotics and covariance tapering in Gaussian random field models, Electronic Journal of Statistics, vol.5, issue.0, pp.238-269, 2011.
DOI : 10.1214/11-EJS607

M. Bevilacqua, R. Vallejos, and D. Velandia, Assessing the significance of the correlation between the components of a bivariate Gaussian random field, Environmetrics, vol.21, issue.023019, pp.545-556, 2015.
DOI : 10.1002/env.2367

R. Furrer, F. Bachoc, and J. Du, Asymptotic properties of multivariate tapering for estimation and prediction, Journal of Multivariate Analysis, vol.149
DOI : 10.1016/j.jmva.2016.04.006
URL : https://hal.archives-ouvertes.fr/hal-01203600

F. Pascual and H. Zhang, Estimation of linear correlation coefficient of two correlated spatial processes, Sankhy¯ a: The Indian Journal of, Statistics, vol.68, pp.307-325, 2006.

S. G. Lehrke and J. K. Ghorai, Large Sample Properties of ML Estimator of the Parameters of Multivariate O???U Random Fields, Communications in Statistics - Theory and Methods, vol.39, issue.4, pp.738-752, 2010.
DOI : 10.1214/aos/1176349272

H. Zhang and W. Cai, When Doesn???t Cokriging Outperform Kriging?, Statistical Science, vol.30, issue.2, pp.176-180, 2015.
DOI : 10.1214/15-STS518

T. Gneiting, W. Kleiber, and M. Schlather, Mat??rn Cross-Covariance Functions for Multivariate Random Fields, Journal of the American Statistical Association, vol.105, issue.491, pp.1167-1177, 2010.
DOI : 10.1198/jasa.2010.tm09420

A. Antognini and M. Zagoraiou, Exact optimal designs for computer experiments via Kriging metamodelling, Journal of Statistical Planning and Inference, vol.140, issue.9, pp.2607-2617, 2010.
DOI : 10.1016/j.jspi.2010.03.027

R. Furrer, M. G. Genton, and D. Nychka, Covariance Tapering for Interpolation of Large Spatial Datasets, Journal of Computational and Graphical Statistics, vol.15, issue.3, pp.502-523, 2006.
DOI : 10.1198/106186006X132178

C. G. Kaufman, M. J. Schervish, and D. W. Nychka, Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets, Journal of the American Statistical Association, vol.103, issue.484, pp.1545-1555, 2008.
DOI : 10.1198/016214508000000959

C. Kaufman, M. Schervish, and D. Nychka, Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets, Journal of the American Statistical Association, vol.103, issue.484, pp.1545-1555, 2008.
DOI : 10.1198/016214508000000959