Conditional versus unconditional mean-squared prediction errors for Gaussian processes with constant but unknown mean

2010 | journal article; research paper. A publication with affiliation to the University of Göttingen.

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​Conditional versus unconditional mean-squared prediction errors for Gaussian processes with constant but unknown mean​
Cullmann, A. D. & Saborowski, J. ​ (2010) 
Environmetrics21(5) pp. 541​-548​.​ DOI: https://doi.org/10.1002/env.1015 

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Authors
Cullmann, Andreas Dominik; Saborowski, Joachim 
Abstract
For prediction in a Gaussian random field, we give an explicit formulation of the conditional mean-squared prediction error (cmspe) If the prediction method is ordinary kriging, we find that this error in most applications is likely to be very close to the ordinary kriging variance This is additionally demonstrated based on a case study Finally, we discuss the difference between these two errors compared to the error introduced by using estimated instead of true covariance parameters Copyright (C) 2009 John Wiley & Sons. Ltd
Issue Date
2010
Journal
Environmetrics 
Organization
Fakultät für Forstwissenschaften und Waldökologie ; Büsgen-Institut ; Abteilung Ökosystemmodellierung ; Abteilung Ökoinformatik, Biometrie und Waldwachstum 
ISSN
1180-4009
Language
English
Subject(s)
Conditional mean squared prediction error; Gaussian random field; Ordinary kriging
Sponsor
DFG [SA 415/3-1, SA 415/3-2]

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