Modeling the Extremes of Bivariate Mixture Distributions With Application to Oceanographic Data

Tendijck, Stan and Eastoe, Emma and Tawn, Jonathan and Randell, David and Jonathan, Philip (2023) Modeling the Extremes of Bivariate Mixture Distributions With Application to Oceanographic Data. Journal of the American Statistical Association, 118 (542). pp. 1373-1384. ISSN 0162-1459

[thumbnail of paper_unblinded]
Text (paper_unblinded)
paper_unblinded.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (1MB)


There currently exist a variety of statistical methods for modeling bivariate extremes. However, when the dependence between variables is driven by more than one latent process, these methods are likely to fail to give reliable inferences. We consider situations in which the observed dependence at extreme levels is a mixture of a possibly unknown number of much simpler bivariate distributions. For such structures, we demonstrate the limitations of existing methods and propose two new methods: an extension of the Heffernan–Tawn conditional extreme value model to allow for mixtures and an extremal quantile-regression approach. The two methods are examined in a simulation study and then applied to oceanographic data. Finally, we discuss extensions including a subasymptotic version of the proposed model, which has the potential to give more efficient results by incorporating data that are less extreme. Both new methods outperform existing approaches when mixtures are present.

Item Type:
Journal Article
Journal or Publication Title:
Journal of the American Statistical Association
Additional Information:
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the American Statistical Association on 21/12/21, available online:
Uncontrolled Keywords:
?? conditional extremesoffshore wave extremesmixture distributionsmultivariate extremesquantile-regressionstatistics and probabilitystatistics, probability and uncertainty ??
ID Code:
Deposited By:
Deposited On:
21 Oct 2021 12:55
Last Modified:
15 Jul 2024 22:02