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http://hdl.handle.net/2183/34323 Nonparametric geostatistical risk mapping
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Fernández-Casal, R., Castillo-Páez, S. & Francisco-Fernández, M. Nonparametric geostatistical risk mapping. Stoch Environ Res Risk Assess 32, 675–684 (2018). https://doi.org/10.1007/s00477-017-1407-y
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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.
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Versión final aceptada de: https://doi.org/10.1007/s00477-017-1407-y
This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00477-017-1407-y
This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00477-017-1407-y
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