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.

引用

页码：37 / 51

页数：15

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