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Analysis of Rainfall Severity and Duration in Victoria, Australia using Non-parametric Copulas and Marginal Distributions

机译:使用非参数Copulas和边际分布分析澳大利亚维多利亚州的降雨强度和持续时间

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摘要

The analysis of joint probability distributions of rainfall characteristics such as severity and duration is important in water resources management. Deriving their distributions using standard statistical techniques are often problematical due to its complexity. Standard methods usually assume that the rainfall characteristics are independent or that their marginal distributions belong to the same family of distributions. The use of copulas based methodologies can circumvent these restrictions and are therefore increasingly popular. However, the copulas and marginal distributions that are commonly used belong to specific parametric families and their adoption could lead to spurious inferences if the underlying assumptions are violated. For this reason, we recommend a nonparamet-ric or semiparametric approach to estimate the joint distribution of rainfall characteristics. In this paper, we introduce and compare several copula-based approaches, each involving a combination of parametric or nonparametric marginal distributions conjoined by a parametric or nonparametric copula. An empirical illustration of the different approaches using rainfall data collected from six stations in the state of Victoria, Australia, demonstrated that a nonparametric approach can often give better results than a purely parametric approach.
机译:降雨特征的严重性和持续时间等联合概率分布的分析在水资源管理中很重要。由于其复杂性,使用标准统计技术推导它们的分布通常是有问题的。标准方法通常假定降雨特征是独立的,或者其边际分布属于同一分布族。使用基于copulas的方法可以规避这些限制,因此越来越受欢迎。但是,常用的copula和边际分布属于特定的参数族,如果违反了基本假设,则采用它们可能导致虚假推断。因此,我们建议采用非参数或半参数方法来估计降雨特征的联合分布。在本文中,我们介绍并比较了几种基于copula的方法,每种方法都将参数或非参数边际分布与参数或非参数copula结合在一起。使用从澳大利亚维多利亚州六个站点收集的降雨数据对不同方法进行的经验说明表明,非参数方法通常比纯参数方法能提供更好的结果。

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