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Nonparametric geostatistical risk mapping.

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Title: Nonparametric geostatistical risk mapping.
Authors: Fernández-Casal, Rubén1 ruben.fcasal@udc.es, Castillo-Páez, Sergio2,3 sacastillo@espe.edu.ec, Francisco-Fernández, Mario1 mariofr@udc.es
Source: Stochastic Environmental Research & Risk Assessment. Mar2018, Vol. 32 Issue 3, p675-684. 10p.
Subjects: Statistical maps, Geological statistics, Risk assessment, Spatial analysis (Statistics), Regression analysis, Variograms, Statistical bootstrapping
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Stochastic+Environmental+Research+%26+Risk+Assessment%22">Stochastic Environmental Research & Risk Assessment</searchLink>. Mar2018, Vol. 32 Issue 3, p675-684. 10p.
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: Statistical maps
        Type: general
      – SubjectFull: Geological statistics
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Spatial analysis (Statistics)
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      – SubjectFull: Regression analysis
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      – SubjectFull: Variograms
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      – SubjectFull: Statistical bootstrapping
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      – TitleFull: Nonparametric geostatistical risk mapping.
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            NameFull: Castillo-Páez, Sergio
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            NameFull: Francisco-Fernández, Mario
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              Text: Mar2018
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