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A location-invariant non-positive moment-type estimator of the extreme value index

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  • Chuandi Liu
  • Chengxiu Ling

Abstract

This paper investigates a class of location invariant non-positive moment-type estimators of extreme value index, which is highly flexible due to the tuning parameter involved. Its asymptotic expansions and its optimal sample fraction in terms of minimal asymptotic mean square error are derived. A small scale Monte Carlo simulation turns out that the new estimators, with a suitable choice of the tuning parameter driven by the data itself, perform well compared to the known ones. Finally, the proposed estimators with a bootstrap optimal sample fraction are applied to an environmental data set.

Suggested Citation

  • Chuandi Liu & Chengxiu Ling, 2019. "A location-invariant non-positive moment-type estimator of the extreme value index," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(5), pages 1166-1176, March.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:5:p:1166-1176
    DOI: 10.1080/03610926.2018.1425448
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