Document Type
Article
Source Publication Title
Journal of Statistical and Econometric Methods
First Page
17
Last Page
40
Abstract
This paper introduces a method for simulating univariate and multivariate Dagum distributions through the method of ??-moments and ??-correlations. A method is developed for characterizing non-normal Dagum distributions with controlled degrees of ??-skew, ??-kurtosis, and ??-correlations. The procedure can be applied in a variety of contexts such as statistical modeling (e.g., income distribution, personal wealth distributions, etc.) and Monte Carlo or simulation studies. Numerical examples are provided to demonstrate that ??-moment-based Dagum distributions are superior to their conventional moment-based analogs in terms of estimation and distribution fitting. Evaluation of the proposed method also demonstrates that the estimates of ??-skew, ?? -kurtosis, and ?? -correlation are substantially superior to their conventional product-moment based counterparts of skew, kurtosis, and Pearson correlation in terms of relative bias and relative efficiency–most notably in the context of heavy-tailed distributions.
Disciplines
Curriculum and Instruction | Education
Publication Date
1-1-2013
Language
English
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Pant, Mohan and Headrick, Todd C., "An L-Moment Based Characterization of the Family of Dagum Distributions" (2013). Curriculum and Instruction Faculty Publications. 44.
https://mavmatrix.uta.edu/curriculuminstruction_facpubs/44