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Generalized fiducial inference for the GEV change-point model

Author

Listed:
  • Xia Cai
  • Yaru Qiao
  • Jiahua Qiao
  • Liang Yan

Abstract

Generalized extreme value (GEV) distribution is used to analyse the maximum from a block of data. It is very useful to describe the unusual event rather than the usual event. In this paper, we propose a change-point detection procedure for GEV distribution based on generalized fiducial inference. The fiducial distribution of the change-point location is constructed. Meanwhile, Markov Chain Monte Carlo method combined with Gibbs sampling and the Metropolis–Hastings algorithm is utilised to estimate the location of the change point and its confidence interval. In addition, the generalized fiducial factor is used to test whether there is a change point. Simulation results show that the proposed generalized fiducial method performs better in accuracy, robustness and the length of confidence intervals. Finally, we apply the proposed method to the annual maximum rainfall data in Beijing.

Suggested Citation

  • Xia Cai & Yaru Qiao & Jiahua Qiao & Liang Yan, 2025. "Generalized fiducial inference for the GEV change-point model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 37(4), pages 1053-1069, October.
    • Handle: RePEc:taf:gnstxx:v:37:y:2025:i:4:p:1053-1069
    DOI: 10.1080/10485252.2024.2387091
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