A Doubling Method for the Generalized Lambda Distribution

Abstract EN

This paper introduces a new family of generalized lambda distributions (GLDs) based on a method of doubling symmetric GLDs.

The focus of the development is in the context of L-moments and L-correlation theory.

As such, included is the development of a procedure for specifying double GLDs with controlled degrees of L-skew, L-kurtosis, and L-correlations.

The procedure can be applied in a variety of settings such as modeling events and Monte Carlo or simulation studies.

Further, it is demonstrated that estimates of L-skew, L-kurtosis, and L-correlation are substantially superior to conventional product-moment estimates of skew, kurtosis, and Pearson correlation in terms of both relative bias and efficiency when heavy tailed distributions are of concern.