The method of self-determined probability weighted moments revisited

Haktanir originally introduced the method of self-determined probability weighted moments as an extension of the traditional method of probability weighted moments for parameter estimation. While this method possesses many advantages, his algorithms introduced certain mathematical manipulations for numerical convenience or based upon special knowledge of the behavior of a data sample. Also, some of these algorithms relied upon inputs from numerical tables that are not widely accessible. To improve the usefulness of this method, new algorithms have been developed that directly implement the relevant equations and do not rely upon external results. In this paper, we show that these features extend the applicability of self-determined probability weighted moments without loss of accuracy in the parameter estimates. Examples from flood peak analysis and extreme wind speed estimation are presented.