I have expanded the current non-iterative sequential simulation toolbox still relying on current algorithms for fast sequential simulation but generalize the type of conditional distributions used, therefore adding more flexibility to sequential simulation. I use a type of spatial dependent bootstrap to generalize the type of local distribution used. This allows imparting other properties to the simulated realizations such as adding local accuracy or imposing connectivity of extreme values. Postscript text extremes extremes1.ps , figures1.tar.Z extremes2.ps , figures2.tar.Z Extreme value statistics for the Earth Sciences. M any natural phenomena exhibit size distributions that are power laws or power law type distributions.
Power laws are specific in the sense that they can exhibit extremely long or heavy tails.
Often, the practitioner is faced with the difficult problem of predicting values far beyond the highest sample value and designing his ''system'' either to profit from them, or to protect against extreme quantiles. I have developed a novel approach to estimating such heavy tails. Because only the most extreme data are useful for studying tails, thresholds must be selected above which the data are modeled as power laws. Left is the Pareto quantile plot, right is the estimators used, which is interpreted as a series of diagnostic regression estimates on the Pareto-quantile plot. The MSE is estimated using either of the listed bootstrap estimates.