Nonparametric geostatistical risk mapping

Abstract:

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.

Año de publicación:

2018

Keywords:

• Nonparametric estimation
• Bias-corrected variogram estimation
• Bootstrap
• Local linear regression
• KRIGING

Fuente:

google

scopus