Favorable Estimators for Fitting Pareto Models: A Study Using Goodness-of-fit Measures with Actual Data

By Vytaras Brazauskas, Robert Serfling. ASTIN Bulletin: The Journal of the International Actuarial Association, 2003

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Estimating conditional tail expectation with actuarial applications in view (2008, background): “Practical aspects of fitting Pareto models to real loss data are investigated in Brazauskas and Serfling (2003).”
Robust fitting of claim severity distributions and the method of trimmed moments (2009, methodology): “In contrast, using specialized derivations of robust estimators, some of the aforementioned applications have been studied by Brazauskas and Serfling (2000a, 2003), Kaiser and Brazauskas (2006), and Dornheim and Brazauskas (2007).”
Modeling Severity and Measuring Tail Risk of Norwegian Fire Claims (2016, methodology): “For the purposes of parameter estimation, construction of quantile-quantile plots, and for computation of other model validation measures, such data clusters were de-grouped using the method described in Brazauskas and Serfling (2003).”
New Goodness-of-Fit Tests for Pareto Distributions (2009, methodology): “For the wind catastrophes data, the scale parameter s = 1.5 was applied by Brazauskas and Serfling (2003) and Hogg and Klugman (1984); Philbrick (1981) applied s = 2.0.”