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Comparative study on excess distribution estimation in iid settings
Responsible organisation
2024 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415XArticle in journal (Refereed) Published
Abstract [en]

This study considers excess distribution estimation in iid settings. There are two ways for the estimation; the fitting to the generalized Pareto distribution and the fully non parametric estimation. The fitting estimator is justified by the approximation proven in the extreme value theory; however, the accuracy depends on how extremely large the target is. The non parametric estimator does not need an approximation and has the advantage of wide applicability. This study conducts both theoretical and numerical comparative study on excess distribution estimation. Asymptotic convergence rates of two estimators are obtained, and the mean integrated squared errors are numerically surveyed by simulation study. An illustrative example of Abisko rainfall amount is presented.

Place, publisher, year, edition, pages
Taylor & Francis , 2024.
Keywords [en]
Excess distribution, extreme value, kernel type estimator, mean squared error, non parametric estimation
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:polar:diva-9091DOI: 10.1080/03610926.2024.2358864OAI: oai:DiVA.org:polar-9091DiVA, id: diva2:1932527
Available from: 2025-01-29 Created: 2025-01-29 Last updated: 2025-06-12Bibliographically approved

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Publisher's full texthttps://doi.org/10.1080/03610926.2024.2358864
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Communications in Statistics - Theory and Methods
Probability Theory and Statistics

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • de-DE
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