J.-P. Chilès, P. Delfiner: Geostatistics: Modeling Spatial Uncertainty

By Denis Allard. Mathematical Geosciences, 2012

2,901 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/916f7a2d955a2931d7d8daf821d14c3ca458d375

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Philippe Renard
2. Finn Lindgren
3. Michael L. Stein
4. Xavier Emery
5. Tilmann Gneiting
6. Martin Schlather
7. Roussos G. Dimitrakopoulos
8. Alexandre Boucher

In Environmental Engineering

1. Tilmann Gneiting
2. Marc G. Genton
3. Håvard Rue
4. Francky Fouedjio
5. Martin Schlather
6. Finn Lindgren
7. Michael L. Stein
8. William Kleiber

In Mechanical Engineering

1. Muhammad Sahimi
2. Pejman Tahmasebi
3. Xavier Emery
4. Roussos G. Dimitrakopoulos
5. Philippe Naveau
6. William Kleiber
7. Olena Babak
8. Michael L. Stein

Citations in the corpus

• Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes (2012, methodology): “This transformation approach is called a spatially varying anamorphosis function [Chilès and Delfiner, 1999], retaining the gamma distribution at individual locations while allowing for any degree of correlation between locations; Berrocal et al. [2008] used a similar model for mesoscale…”
• A Bayesian hierarchical approach to regional frequency analysis (2011, background): “An analogy can be drawn with the common treatment of stationary and isotropic spatial random fields in geostatistics [e.g., Chiles and Delfiner, 1999].”
• Variograms for spatial max-stable random fields (2006, methodology): “(semi) variogram and it has been extensively used by the geostatistic community, see [Wac03], [ CD99 ], [Ste99] and [Cre93].”
• Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network (2009, methodology): “e has a constant mean and the covariance between e(x) and e(x+h) is determined only by the distance vector h (Chilès and Delfiner, 1999).”
• Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging (2011, background): “In this case, the kriging estimates correspond to the conditional expectations of as0 and ys0 given â and ŷ, respectively, under the assumption that the maximum likelihood estimates of the spatial parameters are the true underlying spatial parameters (Chilès and Delfiner 1999).”
• Coupled stochastic weather generation using spatial and generalized linear models (2015, background): “This transformation approach is O(s, t) = [WO(s,t)≥0] called a spatially varying anamorphosis function (Chilès and Delfiner 1999), which retains the gamma distribution at individual locations but allows for spatial correlation between locations.”
• Surface modelling of soil pH (2009, methodology): “Kriging interpolator assumes that the distance, the direction or both between sample points reflect spatial correlation can be used to explain variation in the surface (Chiles and Delfiner, 1999).”
• Sequential Data Assimilation Techniques in Oceanography (2003, methodology): “In geostatistics, a classical way to filter an unknown model bias is to perform the estimation with respect to universality constraints (Chilès and Delfiner, 1999), under the assumption that the bias is uniform on a certain domain, or that it is a combination of translation-invariant base functions.”