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Nonparametric geostatistical risk mapping
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DOI：10.1007/s00477-017-1407-y.png)
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摘要
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In this work, a fully nonparametric geostatistical approach to estimate threshold exceeding probabilities is proposed. To estimate the large-scale variability (spatial trend) of the process, the nonparametric local linear regression estimator, with the bandwidth selected by a method that takes the spatial dependence into account, is used. A bias-corrected nonparametric estimator of the variogram, obtained from the nonparametric residuals, is proposed to estimate the small-scale variability. Finally, a bootstrap algorithm is designed to estimate the unconditional probabilities of exceeding a threshold value at any location. The behavior of this approach is evaluated through simulation and with an application to a real data set.
Keyword:
Local linear regression
Nonparametric estimation
Kriging
Bias-corrected variogram estimation
Bootstrap
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