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A Doubling Method for the Generalized Lambda Distribution
  • Todd C. Headrick, Southern Illinois University Carbondale
  • Mohan Dev Pant, Southern Illinois University Carbondale

Published in ISRN Applied Mathematics, Vol. 2012 at doi:10.5402/2012/725754

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

Citation Information
Todd C. Headrick and Mohan Dev Pant. "A Doubling Method for the Generalized Lambda Distribution" (2012)
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