Nonparametric conditional risk mapping under heteroscedasticity
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929295128002560 |
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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 | A nonparametric procedure to estimate the conditional probability that a nonstationary geostatistical process exceeds a certain threshold value is proposed. The method consists of a bootstrap algorithm that combines conditional simulation techniques with nonparametric estimations of the trend and the variability. The nonparametric local linear estimator, considering a bandwidth matrix selected by a method that takes the spatial dependence into account, is used to estimate the trend. The variability is modeled estimating the conditional variance and the variogram from corrected residuals to avoid the biasses. The proposed method allows to obtain estimates of the conditional exceedance risk in non-observed spatial locations. The performance of the approach is analyzed by simulation and illustrated with the application to a real data set of precipitations in the U.S. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19757 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nonparametric conditional risk mapping under heteroscedasticity Fernández-Casal, Rubén Castillo-Páez, Sergio Francisco-Fernández, Mario Methodology Applications 62H11 (Primary), 62G08, 62G09 (Secondary) A nonparametric procedure to estimate the conditional probability that a nonstationary geostatistical process exceeds a certain threshold value is proposed. The method consists of a bootstrap algorithm that combines conditional simulation techniques with nonparametric estimations of the trend and the variability. The nonparametric local linear estimator, considering a bandwidth matrix selected by a method that takes the spatial dependence into account, is used to estimate the trend. The variability is modeled estimating the conditional variance and the variogram from corrected residuals to avoid the biasses. The proposed method allows to obtain estimates of the conditional exceedance risk in non-observed spatial locations. The performance of the approach is analyzed by simulation and illustrated with the application to a real data set of precipitations in the U.S. |
| title | Nonparametric conditional risk mapping under heteroscedasticity |
| topic | Methodology Applications 62H11 (Primary), 62G08, 62G09 (Secondary) |
| url | https://arxiv.org/abs/2403.19757 |