Model‐based geostatistics

By Peter J. Diggle, Jonathan A. Tawn, Rana Moyeed. 2007

2,621 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/5254f983008dfb7205145c17741499ccbe5e4b72

How researchers see this paper

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

In Economics and Econometrics

1. A. C. Davison
2. Noel Cressie
3. Finn Lindgren
4. Håvard Rue
5. Michael L. Stein
6. Huiyan Sang
7. Raphaël Huser
8. Mathieu Ribatet

In Environmental Engineering

1. A. C. Davison
2. Raphaël Huser
3. Finn Lindgren
4. Noel Cressie
5. Marc G. Genton
6. Håvard Rue
7. Michael L. Stein
8. Huiyan Sang

Citations in the corpus

• Geostatistics of Dependent and Asymptotically Independent Extremes (2013, background): “Classical geostatistics is a well-developed field surveyed in numerous textbooks (e.g., Cressie, 1993; Wackernagel, 2003; Banerjee et al., 2004; Diggle and Ribeiro, 2007; Cressie and Wikle, 2011), with much software available and a wide range of user communities corresponding to its many application”
• Statistical Modelling of Spatial Extremes (2012, methodology): “This approach is common in geostatistics with non-normal response variables (Diggle et al., 1998; Diggle and Ribeiro, 2007), and because of the complexity of the integrations involved is most naturally performed in a Bayesian setting, using Markov chain Monte Carlo algorithms (Robert and Casella,…”
• J.-P. Chilès, P. Delfiner: Geostatistics: Modeling Spatial Uncertainty (2012, neutral): “, the recent Statistics for Spatio-Temporal Data by Cressie and Wickle (2011). Predicting the spatial extent of an extreme quantile of a variable, e.g., precipitation or pollutant concentration, is recognized as one of the most challenging problems in spatial statistics. It is one of the hot researc”
• Inhibitory geostatistical designs for spatial prediction taking account of uncertain covariance structure (2016, methodology): “…earlier, are important for estimating certain parameters of the underlying spatial covariance structure, especially when this includes a nugget variance (Diggle & Ribeiro, 2007, Chapter 8) or a smoothness parameter such as the shape parameter of a Matérn correlation function (Zhu & Stein, 2006).”
• An Overview on Application of Machine Learning Techniques in Optical Networks (2018, background): “7Extensively used in the spatial statistics literature (see [106] for details), kriging is closely related to Gaussian process regression (see [20, Ch.”
• Use of a Gaussian copula for multivariate extreme value analysis: Some case studies in hydrology (2007, methodology): “Such an approach has been proposed by Diggle et al. [14], Cooley [9] and Cooley et al. [10].”
• Continuous Spatial Process Models for Spatial Extreme Values (2010, background): “As with spatial generalized linear models (see, e.g., Diggle et al. (1998)) and with customary Gaussian process models where a nugget is introduced, the implication is that theY (s) are conditionally independent given the second stage parameters (spatial random effects).”
• Fitting complex ecological point process models with integrated nested Laplace approximation (2013, methodology): “To be more specific, models of type a are models of some response variable in which spatial autocorrelation structures form part of the explanatory part of the model (Diggle & Ribeiro 2007).”
• Quantitative approaches in climate change ecology (2011, background): “For the technically inclined, Diggle & Ribeiro (2007) provide a starting point for geostatistical analysis.”
• Testing environmental and genetic effects in the presence of spatial autocorrelation (2014, methodology): “These methods might perform well by the present criteria when properly used, but this may be difficult to assess insofar as they require substantial user intervention on each data set (Diggle and Ribeiro 2007, p. 175).”
• Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland (2010, neutral): “…to this problem is to fit the parameters of the stochastic and the deterministic part of the model jointly by means of maximum-likelihood methods ( Diggle and Ribeiro Jr, 2007; Erdin, 2009); yet for sparse gauge networks and in situations with few wet radar-raingauge pairs, this estimation might…”
• The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015 (2015, background): “We estimated ACT and IRS contributed 19 (15-24)% and 13 (11-16)% respectively, although these interventions had larger proportional contributions where their coverage was high (Extended Data Fig.”