HomeTechniques and Tips@RISK Distribution FittingN/A in Results from Parametric Bootstrapping

# 4.15. N/A in Results from Parametric Bootstrapping

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

In the dialog box for distribution fitting, I selected parametric bootstrapping. The results columns for some distributions shows N/A for bootstrap results, instead of numbers. What does this mean?

N/A for bootstrapping means that the bootstrap failed for that distribution.

If the bootstrap fails for one distribution, it will not necessarily fail for all distributions. The bootstrapping process is done separately for each type of distribution.

What does @RISK consider to be a failure of bootstrapping?

In the bootstrapping process, @RISK takes each fitted distribution and generates a large number of new sample data sets from it, each with the same size as the original data set. It then refits these new data sets and tabulates information about each of the resampled fits. @RISK takes a conservative approach. If it is unable to fit a distribution to even one of the new sample data sets that it generated (meaning that the parameters of that distribution did not converge for that new data set), then @RISK considers that the bootstrap has failed for that distribution.

Does that mean that the fit itself is bad?

Fits aren't good or bad in absolute terms. Instead, you can say that one distribution is better or worse than another for your data set.

Evaluating fits is both objective and subjective. You have the guidance of the fit statistics; for example, see Interpreting AIC Statistics. But your own judgment plays a part, too. For one thing, you have to decide which statistic to use — by a different statistic, fits may rank differently. Also, as you compare your data set to the distributions that @RISK came up with, you might decide to use distribution A rather than B, even though B has a more favorable fit statistic. Maybe A is a better fit than B in a region that you feel is most important, or maybe you have some more general reason for preferring one type of distribution over another.

Last edited: 2016-01-14