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Statistical analysis of the Lognormal-Pareto distribution using Probability Weighted Moments and Maximum Likelihood

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  • Marco Bee

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Abstract

This paper deals with the estimation of the lognormal-Pareto and the lognormal-Generalized Pareto mixture distributions. The log-likelihood function is discontinuous, so that Maximum Likelihood Estimation is not asymptotically optimal. For this reason, we develop an alternative method based on Probability Weighted Moments. We show that the standard version of the method can be applied to the first distribution, but not to the latter. Thus, in the lognormal- Generalized Pareto case, we work out the details of a mixed approach combining Maximum Likelihood Estimation and Probability Weighted Moments. Extensive simulations give precise indications about the relative efficiencies of the methods in various setups. Finally, we apply the techniques to two real datasets in the actuarial and operational risk management fields.

Suggested Citation

  • Marco Bee, 2012. "Statistical analysis of the Lognormal-Pareto distribution using Probability Weighted Moments and Maximum Likelihood," Department of Economics Working Papers 1208, Department of Economics, University of Trento, Italia.
  • Handle: RePEc:trn:utwpde:1208
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    Cited by:

    1. Ramos, Arturo & Sanz-Gracia, Fernando & González-Val, Rafael, 2013. "A new framework for the US city size distribution: Empirical evidence and theory," MPRA Paper 52190, University Library of Munich, Germany.

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    Keywords

    Probability Weighted Moments; Mixed Estimation Method; Lognormal-Pareto Distri- bution; Loss Models;

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