A Multiresolution Gaussian Process Model for the Analysis of Large Spatial Datasets

By D. Nychka, S. Bandyopadhyay, Dorit M. Hammerling, Finn Lindgren, Stephan R. Sain. 2015

353 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/609805c8591edf6ecae767214e07378dbb60419c

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Citations in the corpus

Likelihood approximation with hierarchical matrices for large spatial datasets (2017, methodology): “rther improve on these issues, other methods that have been recently developed include the nearest-neighbor Gaussian process models [17], low-rank update [66], multiresolution Gaussian process models [57], equivalent kriging [42], multi-level restricted Gaussian maximum likelihood estimators [14], a”
Basis-Function Models in Spatial Statistics (2021, methodology): “Exploiting this is central to what are commonly known as Fixed Rank Kriging (FRK) models (Zammit-Mangion & Cressie, 2021), LatticeKrig models (Nychka et al., 2015), and stochastic partial differential equation (SPDE) models (Lindgren et al., 2011); further discussion of these models is given in…”
Second-order non-stationary modeling approaches for univariate geostatistical data (2017, background): “Matsuo et al. (2011) and Nychka et al. (2015) extend the modeling approach of Nychka et al. (2002) to take into account large irregularly spaced datasets.”
A Case Study Competition Among Methods for Analyzing Large Spatial Data (2017, methodology): “This study, while thorough, is non-comprehensive in that other methods for large spatial data (e.g., Sang and Huang 2012; Stein et al. 2013; Kleiber and Nychka 2015; CastrillonCandás et al. 2016; Sun and Stein 2016; Litvinenko et al. 2017) were not included.”
When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018, background): “Hybrid approximations can also be conducted through a coarse-to-fine process, resulting in a hierarchical structure with multiple layers yielding multiresolution [40], [167]–[169].”