Interpolation of spatial data: a stochastic or a deterministic problem?

Interpolation of spatial data is a very general mathematical problem with various applications. In geostatistics, it is assumed that the underlying structure of the data is a stochastic process which leads to an interpolation procedure known as kriging. This method is mathematically equivalent to ke...

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Hauptverfasser: Scheuerer, Michael (VerfasserIn) , Schaback, Robert (VerfasserIn) , Schlather, Martin (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 7 February 2013
In: European journal of applied mathematics
Year: 2013, Jahrgang: 24, Heft: 4, Pages: 601-629
ISSN:1469-4425
DOI:10.1017/S0956792513000016
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1017/S0956792513000016
Verlag, lizenzpflichtig, Volltext: https://www.cambridge.org/core/journals/european-journal-of-applied-mathematics/article/interpolation-of-spatial-data-a-stochastic-or-a-deterministic-problem/D1EA0D2A6379B7737FCA054F14172E7A
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Verfasserangaben:M. Scheuerer, R. Schaback, M. Schlather
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Zusammenfassung:Interpolation of spatial data is a very general mathematical problem with various applications. In geostatistics, it is assumed that the underlying structure of the data is a stochastic process which leads to an interpolation procedure known as kriging. This method is mathematically equivalent to kernel interpolation, a method used in numerical analysis for the same problem, but derived under completely different modelling assumptions. In this paper we present the two approaches and discuss their modelling assumptions, notions of optimality and different concepts to quantify the interpolation accuracy. Their relation is much closer than has been appreciated so far, and even results on convergence rates of kernel interpolants can be translated to the geostatistical framework. We sketch different answers obtained in the two fields concerning the issue of kernel misspecification, present some methods for kernel selection and discuss the scope of these methods with a data example from the computer experiments literature.
Beschreibung:Gesehen am 07.02.2022
Beschreibung:Online Resource
ISSN:1469-4425
DOI:10.1017/S0956792513000016