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

• 成果类型: Article
• 署名作者: Tendijck, Stan; Eastoe, Emma; Tawn, Jonathan; Randell, David; Jonathan, Philip
• 署名单位: Lancaster University; Royal Dutch Shell; Royal Dutch Shell
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1996379
• 发表日期: 2023
• 页码: 1373-1384
• 关键词: dependence; inference

摘要

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.