Fixed rank kriging for very large spatial data sets

By Noel Cressie, G. Jóhannesson. 2008

1,099 citations recorded by the source corpus.

Subfields: Applied Mathematics, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/37e7b513fbc432544cb8f8b1fbd2b436fb26bb47

How researchers see this paper

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

In Applied Mathematics

In Environmental Engineering

In Statistics and Probability

Citations in the corpus

A full scale approximation of covariance functions for large spatial data sets (2012, background): “, Higdon, 2002), low rank splines or basis functions (e.g., Wikle and Cressie, 1999; Ver Hoef et al., 2004; Kammann and Wand, 2003; Cressie and Johannesson, 2008), and predictive process models (Banerjee et al.”
A General Framework for Vecchia Approximations of Gaussian Processes (2017, positive): “s (Rue and Held, 2005; Lindgren et al., 2011; Nychka et al., 2015), and low-rank matrices (e.g., Higdon, 1998; Wikle and Cressie, 1999; Quinonero-~ Candela and Rasmussen, 2005; Banerjee et al., 2008; Cressie and Johannesson, 2008; Katzfuss and Cressie, 2011). Several other approaches are described i”
Gaussian predictive process models for large spatial data sets (2008, methodology): “The Sherman–Woodbury–Morrison matrix identities have been used in other low rank kriging approaches with marginalized likelihoods, e.g. Cressie and Johannesson (2008).”
Bayesian Inference for the Spatial Random Effects Model (2011, methodology): “Cressie and Johannesson (2006, 2008) suggested another approach, for which kriging can be done exactly.”
When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018, methodology): “4, the SoR approximation and the RVMtype models [57]–[59] impose too restrictive assumptions to the training and test data such that they produce overconfident prediction variances when leaving the training data.”
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets (2014, background): “Please go towww.tandfonline.com/r/JASA Wand 2003; Rasmussen and Williams 2005; Stein 2007, 2008; Banerjee et al. 2008; Crainiceanu et al. 2008; Cressie and Johannesson 2008; Finley, Banerjee, and McRoberts 2009) by regressing the original (parent) process on its realizations over a smaller set of r…”
Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB (2024, neutral): “Reduced-rank approaches have seen considerable use in bioinformatics (e.g., Smith, Cullis, and Thompson 2001; Buettner et al. 2015) and spatial statistics (Cressie and Johannesson 2008; Banerjee, Gelfand, Finley, and Sang 2008).”
Nonstationary covariance models for global data (2008, methodology): “The model used in Cressie and Johannesson (2008) has 396 parameters, but it is not clear to us that even this model can accurately capture the local behavior of the process.”
Hierarchical statistical modeling of big spatial datasets using the exponential family of distributions (2013, methodology): “First we calculatedK0 such that||SK0S⊤−Σν|| was minimized, where|| · || is the Frobenius norm (e.g., Cressie and Johannesson, 2008).”
Machine learning for digital soil mapping: Applications, challenges and suggested solutions (2020, background): “Finally, geostatistical models are computationally demanding if the sample size and/or the number of prediction locations are large (Cressie and Johannesson, 2008).”
Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach (2012, methodology): “Kriging prediction is challenging for large datasets (Cressie and Johannesson 2008) and here we used as a predictor the last four daily observations recorded at the 513 stations.”