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4.2. Bootstrapping for Distribution Fitting

Applies to: @RISK 6.x/7.x, Professional and Industrial Editions

I am a user of @RISK, and I wonder if it might be used for a nonparametric bootstrap method for analyzing a data set.

Beginning with release 6.0, @RISK offers parametric bootstrapping. Compared to nonparametric bootstrapping, parametric bootstrapping requires less resampling and is more robust with smaller data sets. You can get parameter confidence intervals as well as goodness-of-fit statistics.

Because it is computationally intensive, parametric bootstrapping is turned off by default in @RISK. You can select it on the Bootstrapping tab of the dialog for fitting distributions. Please see "Appendix A: Distribution Fitting" in the @RISK user manual or help file. There's also a nice picture in 15.3 Bootstrapping from Penn State's Eberly College of Science.

See also: N/A in Results from Parametric Bootstrapping

Last edited: 2018-11-09

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