Rights statement: 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: http://www.tandfonline.com/10.1080/01621459.2021.1996379
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Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Modeling the Extremes of Bivariate Mixture Distributions With Application to Oceanographic Data
AU - Tendijck, Stan
AU - Eastoe, Emma
AU - Tawn, Jonathan
AU - Randell, David
AU - Jonathan, Philip
N1 - 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: http://www.tandfonline.com/10.1080/01621459.2021.1996379
PY - 2023/6/30
Y1 - 2023/6/30
N2 - 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.
AB - 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.
KW - Conditional extremes
KW - Offshore wave extremes
KW - Mixture distributions
KW - Multivariate extremes
KW - Quantile-regression
U2 - 10.1080/01621459.2021.1996379
DO - 10.1080/01621459.2021.1996379
M3 - Journal article
VL - 118
SP - 1373
EP - 1384
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
SN - 0162-1459
IS - 542
ER -