Palisade Knowledge Base

HomeTechniques and Tips@RISK Distribution FittingTechnical Details of Distribution Fitting

4.20. Technical Details of Distribution Fitting

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

How does @RISK estimate distribution parameters? Can you give me any details?

In general, we use Maximum Likelihood Estimators (MLEs). For details, please use the Search tab in @RISK help to find the topic "Sample Data — Maximum Likelihood Estimators (MLEs)". After reading, click the Next button at the top and continue reading the subtopic "Modifications to the MLE Method".

For references to methods that we use, search Help for the term "Merran" and click on the topic "Distributions and Distribution Fitting" in the search results.

It's important to realize that not all distributions are fit in exactly the same way. In the more than 30 years we've been improving @RISK, we have developed many proprietary tweaks to the standard algorithms, to do a better job of fitting particular distributions. These let the fit proceed more efficiently, handle cases where the standard MLE algorithms break down, and so on.

Although the fine details of our fitting algorithms are proprietary, the fit results include many popular goodness-of-fit statistics, including AIC, Anderson-Darling, BIC, χ², Kolmogorov-Smirnov, and RMS. For details of these statistics, see the "Fit Statistics" topic in @RISK help, as well as several articles in the @RISK Distribution Fitting chapter of this Knowledge Base.

Last edited: 2016-08-09

This page was: Helpful | Not Helpful