10 citations · 15 across the 11 of their papers we have counts for
5 papers · 1 filter
Geostatistics for large datasets on Riemannian manifolds: a matrix-free approach
Mike Pereira, Nicolas Desassis, Denis Allard
Large or very large spatial (and spatio-temporal) datasets have become common place in many environmental and climate studies. These data are often collected in non-Euclidean space…
A general framework for SPDE-based stationary random fields
Ricardo Carrizo Vergara, Denis Allard, Nicolas Desassis
This paper presents theoretical advances in the application of the Stochastic Partial Differential Equation (SPDE) approach in geostatistics. We show a general approach to construc…
Combining covariance tapering and lasso driven low rank decomposition for the kriging of large spatial datasets
Thomas Romary, Nicolas Desassis
Large spatial datasets are becoming ubiquitous in environmental sciences with the explosion in the amount of data produced by sensors that monitor and measure the Earth system. Con…
A pairwise likelihood approach for the empirical estimation of the underlyingvariograms in the plurigaussian models
Nicolas Desassis, Didier Renard, Hélène Beucher +2
The plurigaussian model is particularly suited to describe categorical regionalized variables. Starting from a simple principle, the thresh-olding of one or several Gaussian random…
Incorporating a contrast in the Bayesian formula: What consequences for the MAP estimator and the posterior distribution? Applications in spatial statistics
S. Soubeyrand, F. Carpentier, N. Desassis +1
In order to estimate model parameters and circumvent possible difficulties encountered with the likelihood function, we propose to replace the likelihood in the formula of the post…