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A likelihood for correlated extreme series

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  • L. Zhu
  • X. Liu
  • R. Lund

Abstract

This paper develops a likelihood for sequences of extremes when observations are dependent in time. The likelihood allows researchers to obtain more realistic standard errors of the generalized extreme‐value parameters. As a motivating example, annual minimum temperatures are examined from the Faraday/Vernadsky research station in Antarctica. Here, the year‐to‐year correlation in the series is about 0.46. Our likelihood allows the series to have temporal correlation but also keeps a generalized extreme‐value marginal distribution at each point in time. An analysis of the Faraday/Vernadsky annual minimum temperatures is conducted. While the standard error of the estimated trend increases when dependence is taken into account, it does not change the correlation‐ignored inference that annual minimum temperatures at the station are increasing.

Suggested Citation

  • L. Zhu & X. Liu & R. Lund, 2019. "A likelihood for correlated extreme series," Environmetrics, John Wiley & Sons, Ltd., vol. 30(4), June.
    • Handle: RePEc:wly:envmet:v:30:y:2019:i:4:n:e2546
    DOI: 10.1002/env.2546
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    Cited by:

    1. Brook T. Russell & Whitney K. Huang, 2021. "Modeling short‐ranged dependence in block extrema with application to polar temperature data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.

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