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An L-Moment Based Characterization of the Family of Dagum Distributions
Journal of Statistical and Econometric Methods (2013)
  • Mohan D. Pant, University of Texas at Arlington
  • Todd C. Headrick, Southern Illinois University Carbondale
This paper introduces a method for simulating univariate and multivariate Dagum distributions through the method of 𝐿-moments and 𝐿-correlations. A method is developed for characterizing non-normal Dagum distributions with controlled degrees of 𝐿-skew, 𝐿-kurtosis, and 𝐿-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 𝐿-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 𝐿-skew, 𝐿-kurtosis, and 𝐿-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.
  • Skew,
  • L-Skew,
  • Kurtosis,
  • L-Kurtosis,
  • Correlation,
  • L-correlation
Publication Date
Citation Information
Mohan D. Pant and Todd C. Headrick. "An L-Moment Based Characterization of the Family of Dagum Distributions" Journal of Statistical and Econometric Methods Vol. 2 Iss. 3 (2013)
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