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Estimating Extreme Wave Surges in the Presence of Missing Data

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  • James H. McVittie
  • Orla A. Murphy

Abstract

The block maxima approach, which consists of dividing a series of observations into equal‐sized blocks to extract the block maxima, is commonly used for identifying and modeling extreme events using the generalized extreme value (GEV) distribution. In the analysis of coastal wave surge levels, the underlying data that generate the block maxima typically have missing observations. Consequently, the observed block maxima may not correspond to the true block maxima, yielding biased estimates of the GEV distribution parameters. Various parametric modeling procedures are proposed to account for the presence of missing observations under a block maxima framework. The performance of these estimators is compared through an extensive simulation study and illustrated by an analysis of extreme wave surges in Atlantic Canada.

Suggested Citation

  • James H. McVittie & Orla A. Murphy, 2025. "Estimating Extreme Wave Surges in the Presence of Missing Data," Environmetrics, John Wiley & Sons, Ltd., vol. 36(6), September.
    • Handle: RePEc:wly:envmet:v:36:y:2025:i:6:n:e70036
    DOI: 10.1002/env.70036
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    References listed on IDEAS

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    1. F. Ayiah-Mensah & R. Minkah & L. Asiedu & F. O. Mettle, 2021. "An Enhanced Method for Tail Index Estimation under Missingness," Journal of Applied Mathematics, Hindawi, vol. 2021, pages 1-13, July.
    2. Mortaza Jamshidian & Siavash Jalal, 2010. "Tests of Homoscedasticity, Normality, and Missing Completely at Random for Incomplete Multivariate Data," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 649-674, December.
    3. F. Ayiah-Mensah & R. Minkah & L. Asiedu & F. O. Mettle, 2021. "An Enhanced Method for Tail Index Estimation under Missingness," Journal of Applied Mathematics, John Wiley & Sons, vol. 2021(1).
    4. N. Beck & C. Genest & J. Jalbert & M. Mailhot, 2020. "Predicting extreme surges from sparse data using a copula‐based hierarchical Bayesian spatial model," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
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