NDLI: Incorporating parameter uncertainty into prediction intervals for spatial data modeled via a parametric variogram

Content Provider	Springer Nature Link
Author	Wang, Fujun Wall, Melanie M.
Copyright Year	2003
Abstract	In spatial predictions, researchers usually treat the estimated theoretical variogram parameters as known without error and ignore the variability of the parameter estimators. Although the prediction is still unbiased, the prediction error is usually underestimated. Therefore, the coverage probability of the prediction interval usually is lower than the nominal probability. A simulation study is performed to show how the coverage probability for prediction relates to the true range and sill of an exponential variogram. This article proposes two parametric bootstrap methods to incorporate the variability of the corresponding parameter estimators. A simulation study is performed to evaluate the coverage probability of these proposed methods. Finally, we apply the parametric bootstrap methods to a real dataset and compare the results with those from naive (i.e., treating estimated parameters as known) and Bayesian methods.
Starting Page	296
Ending Page	309
Page Count	14
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	8
Issue Number	3
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2003-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bootstrap Coverage probability Kriging Mean squared prediction error Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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