Nonparametric Conditional Risk Mapping Under Heteroscedasticity

Rubén Fernández-Casal (), Sergio Castillo-Páez () and Mario Francisco-Fernández ()
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Rubén Fernández-Casal: Universidade da Coruña
Sergio Castillo-Páez: Universidad de las Fuerzas Armadas ESPE
Mario Francisco-Fernández: Universidad de las Fuerzas Armadas ESPE

Journal of Agricultural, Biological and Environmental Statistics, 2024, vol. 29, issue 1, No 4, 56-72

Abstract: Abstract 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.

Keywords: Bootstrap; Conditional simulation; Local linear estimation; Bias correction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13253-023-00555-0

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