Nonparametric Conditional Risk Mapping Under Heteroscedasticity
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Nonparametric Conditional Risk Mapping Under HeteroscedasticityData
2024-03Cita bibliográfica
Fernández-Casal, R., Castillo-Páez, S. & Francisco-Fernández, M. Nonparametric Conditional Risk Mapping Under Heteroscedasticity. JABES 29, 56–72 (2024). https://doi.org/10.1007/s13253-023-00555-0
Resumo
[Absctract]: 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 USA.Supplementary materials accompanying this paper appear on-line.
Palabras chave
Bootstrap
Conditional simulation
Local linear estimation
Bias correction
Conditional simulation
Local linear estimation
Bias correction
Descrición
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature
Versión do editor
Dereitos
Atribución 3.0 España
ISSN
1537-2693
1085-7117
1085-7117