NSVineCopula：非平稳多变量相关性建模的R包

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software Pub Date : 2025-04-17 DOI:10.1016/j.envsoft.2025.106474

Q. Zhang , Y.P. Li , G.H. Huang , X.M. Huang , H. Wang , Z. Wang , Z.P. Xu , Y.Y. Wang , Z.Y. Shen

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引用次数: 0

摘要

vine copula是一种灵活的多变量相关性模拟方法。然而，只有少数研究关注非平稳，并全面发展了非平稳的藤联函数。在这项研究中，一个新的R包，NSVineCopula开发并提出。在NSVineCopula中考虑了具有36个二元联结函数的正则-藤和可拉-藤结构。该包能够捕获非平稳的多变量依赖，为每个二元联结提供时变参数，并量化条件概率。值得注意的是，NSVineCopula提供了一种简单的方法来采样非平稳的藤丛。通过两个案例对NSVineCopula的能力进行了评价：(1)复合干热极端条件下的农业干旱风险评估和水位预测。结果表明了NSVineCopula在非平稳多变量依赖分析中的优势，并突出了NSVineCopula在许多领域的潜力。总体而言，NSVineCopula可以为非平稳多变量依赖的建模提供有价值的鲁棒功能。

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NSVineCopula: R package for modeling non-stationary multivariate dependence

A vine copula is a flexible method for multivariate dependence simulations that assumes stationarity. However, only a few studies have focused on non-stationarity and comprehensively developed nonstationary vine copula functions. In this study, a novel R package, NSVineCopula was developed and presented. Canonical-vine and Drawable-vine structure with 36 bivariate copula functions were considered in NSVineCopula. This package is capable of capturing non-stationary multivariate dependence, providing time-varying parameters for each bivariate copula, and quantifying the conditional probability. Notably, NSVineCopula provides a simple way for sampling non-stationary vine copulas. The capability of NSVineCopula was evaluated through two case studies: (1) agricultural drought risk assessment under compound dry-hot extreme conditions and water level prediction. The results demonstrate the advantages of NSVineCopula in non-stationary multivariate dependence analysis, and highlights the potential of NSVineCopula in many fields. Overall, NSVineCopula can provide valuable and robust functionalities for modeling nonstationary multivariate dependence.

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来源期刊

Environmental Modelling & Software 工程技术-工程：环境

CiteScore

9.30

自引率

8.20%

发文量

241

审稿时长

60 days

期刊介绍： Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.