Bayes Quantile Estimation and Threshold Selection for the Generalized Pareto Family

James Pickands
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James Pickands: University of Pennsylvania

A chapter in Extreme Value Theory and Applications, 1994, pp 123-138 from Springer

Abstract: Abstract There is always a trade off between computational and statistical efficiency. The phenomenal recent advances in computing have shifted the advantage toward Bayesian methods of estimation. We propose an interactive program for analyzing observations above a high threshold. The first phase of the analysis is the “identification” phase. Using parameter estimators which are not too efficient but easy to compute, we perform many iterations to find the empirical optimal combination choice of threshold and choice of monotone increasing function transformation. We review existing methods of estimation. The second phase is the “estimation” phase. We examine the use of Bayes estimators for this phase. We also suggest a method of finding confidence regions.

Keywords: Generalize Pareto Distribution; Threshold Selection; Statistical Efficiency; Quantile Estimation; Extreme Order Statistic (search for similar items in EconPapers)
Date: 1994
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4613-3638-9_7

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DOI: 10.1007/978-1-4613-3638-9_7

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