Bias-corrected maximum likelihood estimation of the parameters of the generalized Pareto distribution

David Giles, Hui Feng and Ryan T. Godwin

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 8, 2465-2483

Abstract: We derive analytic expressions for the biases, to O(n−1), of the maximum likelihood estimators of the parameters of the generalized Pareto distribution. Using these expressions to bias-correct the estimators in a selective manner is found to be extremely effective in terms of bias reduction, and can also result in a small reduction in relative mean squared error (MSE). In terms of remaining relative bias, the analytic bias-corrected estimators are somewhat less effective than their counterparts obtained by using a parametric bootstrap bias correction. However, the analytic correction out-performs the bootstrap correction in terms of remaining %MSE. It also performs credibly relative to other recently proposed estimators for this distribution. Taking into account the relative computational costs, this leads us to recommend the selective use of the analytic bias adjustment for most practical situations.

Date: 2016
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Citations: View citations in EconPapers (9)

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DOI: 10.1080/03610926.2014.887104

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