Bayesian nonstationary spatial modeling for very large datasets
With the proliferation of modern high-resolution measuring instruments mounted on satellites, planes, ground-based vehicles, and monitoring stations, a need has arisen for statistical methods suitable for the analysis of large spatial datasets observed on large spatial domains. Statistical analyses...
Gespeichert in:
| 1. Verfasser: | |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
11 February 2013
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| In: |
Environmetrics
Year: 2013, Jahrgang: 24, Heft: 3, Pages: 189-200 |
| ISSN: | 1099-095X |
| DOI: | https://doi.org/10.1002/env.2200 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/https://doi.org/10.1002/env.2200 Verlag, lizenzpflichtig, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/env.2200 
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| Verfasserangaben: | Matthias Katzfuss |
MARC
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| 520 | |a With the proliferation of modern high-resolution measuring instruments mounted on satellites, planes, ground-based vehicles, and monitoring stations, a need has arisen for statistical methods suitable for the analysis of large spatial datasets observed on large spatial domains. Statistical analyses of such datasets provide two main challenges: first, traditional spatial-statistical techniques are often unable to handle large numbers of observations in a computationally feasible way; second, for large and heterogeneous spatial domains, it is often not appropriate to assume that a process of interest is stationary over the entire domain. We address the first challenge by using a model combining a low-rank component, which allows for flexible modeling of medium-to-long-range dependence via a set of spatial basis functions, with a tapered remainder component, which allows for modeling of local dependence using a compactly supported covariance function. Addressing the second challenge, we propose two extensions to this model that result in increased flexibility: first, the model is parameterized on the basis of a nonstationary Matérn covariance, where the parameters vary smoothly across space; second, in our fully Bayesian model, all components and parameters are considered random, including the number, locations, and shapes of the basis functions used in the low-rank component. Using simulated data and a real-world dataset of high-resolution soil measurements, we show that both extensions can result in substantial improvements over the current state-of-the-art. Copyright © 2013 John Wiley & Sons, Ltd. | ||
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