In situations where both extreme and non-extreme data are of interest, modelling the whole data set accurately is important. In a univariate framework, modelling the bulk and tail of a distribution has been extensively studied before. However, when more than one variable is of concern, models that aim specifically at capturing both regions correctly are scarce in the literature. A dependence model that blends two copulas with different characteristics over the whole range of the data support is proposed. One copula is tailored to the bulk and the other to the tail, with a dynamic weighting function employed to transition smoothly between them. Tail dependence properties are investigated numerically and simulation is used to confirm that the blended model is sufficiently flexible to capture a wide variety of structures. The model is applied to study the dependence between temperature and ozone concentration at two sites in the UK and compared with a single copula fit. The proposed model provides a better, more flexible, fit to the data, and is also capable of capturing complex dependence structures.

翻译：在需要同时关注极端数据与非极端数据的情况下，准确建模整个数据集至关重要。在单变量框架中，对分布的主体和尾部进行建模已有广泛研究。然而，当涉及多个变量时，旨在同时正确捕捉这两个区域的模型在文献中较为罕见。本文提出一种依赖关系模型，该模型在整个数据支持范围内融合两种具有不同特性的联接函数。一种联接函数针对数据主体进行定制，另一种则针对尾部，通过动态权重函数实现两者之间的平滑过渡。通过数值研究分析了尾部依赖性质，并利用模拟验证了该混合模型在捕捉多种结构方面具有足够的灵活性。将该模型应用于英国两个地点温度和臭氧浓度之间的依赖关系研究，并与单一联接函数的拟合结果进行了比较。结果表明，所提模型对数据提供了更优、更灵活的拟合，且能够捕捉复杂的依赖结构。