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Spatial sampling design under the infill asymptotic framework
Author(s) -
Zhu Zhengyuan,
Zhang Hao
Publication year - 2006
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.772
Subject(s) - asymptotic analysis , sampling (signal processing) , best linear unbiased prediction , computation , sample size determination , mathematical optimization , sample (material) , infill , covariance , sampling design , mathematics , computer science , optimal design , function (biology) , statistics , econometrics , algorithm , machine learning , engineering , population , demography , structural engineering , filter (signal processing) , sociology , computer vision , selection (genetic algorithm) , chemistry , chromatography , evolutionary biology , biology
We study optimal sample designs for prediction with estimated parameters. Recent advances in the infill asymptotic theory provide a deeper understanding of the finite sample behavior of prediction and estimation. By incorporating these known asymptotic results, we modify some existing design criteria for estimation of covariance function and best linear unbiased prediction. These modified criteria could significantly reduce the computation time necessary for finding an optimal design. We illustrate our approach through both a real experiment in agriculture and simulation. Copyright © 2005 John Wiley & Sons, Ltd.