Fernández-Casal, RubénCastillo-Páez, SergioFrancisco-Fernández, Mario2023-11-232023-11-232018Fernández-Casal, R., Castillo-Páez, S. & Francisco-Fernández, M. Nonparametric geostatistical risk mapping. Stoch Environ Res Risk Assess 32, 675–684 (2018). https://doi.org/10.1007/s00477-017-1407-yhttp://hdl.handle.net/2183/34323Versión final aceptada de: https://doi.org/10.1007/s00477-017-1407-yThis version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00477-017-1407-yIn 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.engTodos os dereitos reservados. All rights reserved.Local linear regressionNonparametric estimationKrigingBias-corrected variogram estimationBootstrapjournal articleopen access10.1007/s00477-017-1407-y