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Improving on Estimation for the Generalized Pareto Distribution

Summary: [This abstract is based on the author’s abstract.] A method recently proposed for the estimation of parameters of the generalized Pareto distribution (GPD) widely used to model exceedances over threshold is based on the likelihood and empirical Bayesian methods. The method is free from computational problems encountered in traditional approaches, and while it performs well in most situations, it may perform poorly for heavy-tailed distributions. A modification is proposed for the method to improve its adaptability.

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  • Topics: Statistics
  • Keywords: Bias, Bayesian methods, Estimation, Maximum likelihood estimate (MLE), Parameters
  • Author: Zhang, Jin
  • Journal: Technometrics