  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.
