@Risk: Bootstrapping for Distribution Fitting
Beginning with release 6.0, @RISK offers parametric bootstrapping. Parametric bootstrapping is more efficient than nonparametric bootstrapping requires fewer resamples performs better with small data sets. It also provides parameter confidence intervals and goodness-of-fit statistics. Because it is computationally intensive, @RISK disables parametric bootstrapping by default. You can enable it on the Bootstrapping tab when fitting distributions. For details, see Appendix A: Distribution Fitting in the @RISK user manual or help file.