Prediction intervals for integrals of Gaussian random fields

Victor De Oliveira and Bazoumana Kone

Computational Statistics & Data Analysis, 2015, vol. 83, issue C, 37-51

Abstract: Methodology is proposed for the construction of prediction intervals for integrals of Gaussian random fields over bounded regions (called block averages in the geostatistical literature) based on observations at a finite set of sampling locations. Two bootstrap calibration algorithms are proposed, termed indirect and direct, aimed at improving upon plug-in prediction intervals in terms of coverage probability. A simulation study is carried out that illustrates the effectiveness of both procedures, and these procedures are applied to estimate block averages of chromium traces in a potentially contaminated region in Switzerland.

Keywords: Block average; Bootstrap calibration; Change of support problem; Geostatistics; Kriging; Spatial average (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:83:y:2015:i:c:p:37-51

DOI: 10.1016/j.csda.2014.09.013

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