Prediction intervals for integrals of Gaussian random fields

被引：6
作者：
De Oliveira, Victor [1 ]
Kone, Bazoumana [1 ,2 ]
机构：
[1] Univ Texas San Antonio, Dept Management Sci & Stat, San Antonio, TX 78249 USA
[2] PPD, Austin, TX 78744 USA
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2015年 / 83卷
基金：
美国国家卫生研究院;
关键词：
Block average; Bootstrap calibration; Change of support problem; Geostatistics; Kriging; Spatial average; COVARIANCE; LIMITS;
D O I：
10.1016/j.csda.2014.09.013
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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. (C) 2014 Elsevier B.V. All rights reserved.
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页码：37 / 51
页数：15
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