A full scale approximation of covariance functions for large spatial data sets

By Huiyan Sang, Jianhua Z. Huang. 2012

267 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/15e436ee7cb55fd497689a5262eb205753cca42c

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Citations in the corpus

Practical options for selecting data-driven or physics-based prognostics algorithms with reviews (2015, methodology): “On the other hand, Huang’s model is defined as da dN ¼ C ΔKeq β;n;σY m ΔKth mh i where m, C, ΔKth, β, n, and σY are model parameters.”
A General Framework for Vecchia Approximations of Gaussian Processes (2017, background): “SCS is an extension of the predictive process, which tends to work well in such “smooth” situations, because it is equivalent to a Nystr¨om approximation of the leading terms of the Karhunen-Lo´eve expansion of y ( · ) (Sang and Huang, 2012).”
Local Gaussian Process Approximation for Large Computer Experiments (2013, methodology): “ramacy and Polson, 2011), xed rank kriging (Cressie and Johannesson, 2008), and the use of compactly supported covariance (CSC) matrices and fast sparse linear algebra packages (Kaufman et al., 2012; Sang and Huang, 2012). Our contribution has aspects in common with all of these approaches and sever”
A Unified Framework for Fitting Bayesian Semiparametric Models to Arbitrarily Censored Survival Data, Including Spatially Referenced Data (2017, background): “2008), sparse approximations (Kaufman, Schervish, andNychka 2008), and the full-scale approximation (Sang and Huang 2012).”
A Case Study Competition Among Methods for Analyzing Large Spatial Data (2017, methodology): “1) can be calculated using parallel computing while Katzfuss and Hammerling (2017) and Katzfuss (2017) develop a basisfunction approach that lends itself to distributed computing. Alternatively, Barbian and Assunção (2017) and Guhaniyogi and Banerjee (2018) propose dividing the data into a large num”
A class of Matérn-like covariance functions for smooth processes on a sphere (2015, methodology): “For computational efficiency with large data and large nc and np values, the proposed model can be fitted with recently developed computational techniques, such as covariance tapering (Furrer et al., 2006; Kaufman et al., 2008) or full scale covariance approximation (Sang and Huang, 2012).”
Likelihood approximation with hierarchical matrices for large spatial datasets (2017, background): “During the past two decades, a large amount of research has been devoted to tackling the aforementioned computational challenge of developing scalable methods: for example, covariance tapering [21, 36, 60], likelihood approximations in both the spatial [64] and spectral [19] domains, latent processe”
Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning (2011, background): “…recently been made toward computation-26 ally tractable, approximate GP (regression) inference in large data settings [e.g., Haaland and Qian (2011), Sang and Huang (2012)], their application to canonical computer experiments problems such as design and optimization remains a topic of future study.”
A Multiresolution Gaussian Process Model for the Analysis of Large Spatial Datasets (2015, methodology): “An insightful approach 105 was suggested in Stein (2008) and later in Sang and Huang (2011) where a low rank 106 process was combined with a process that has a compactly supported covariance.”