Nonparametric geostatistical risk mapping
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911768372051968 |
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| author | Fernández-casal, Rubén Castillo-Páez, Sergio Francisco-Fernández, Mario |
| author_facet | Fernández-casal, Rubén Castillo-Páez, Sergio Francisco-Fernández, Mario |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_17770 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nonparametric geostatistical risk mapping Fernández-casal, Rubén Castillo-Páez, Sergio Francisco-Fernández, Mario Methodology Applications 62H11 (Primary), 62G08, 62G09 (Secondary) 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. |
| title | Nonparametric geostatistical risk mapping |
| topic | Methodology Applications 62H11 (Primary), 62G08, 62G09 (Secondary) |
| url | https://arxiv.org/abs/2401.17770 |