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.
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© 1994 Kluwer Academic Publishers
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Pickands, J. (1994). Bayes Quantile Estimation and Threshold Selection for the Generalized Pareto Family. In: Galambos, J., Lechner, J., Simiu, E. (eds) Extreme Value Theory and Applications. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-3638-9_7
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DOI: https://doi.org/10.1007/978-1-4613-3638-9_7
Publisher Name: Springer, Boston, MA
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