相关不确定性下决策敏感性的copula分析

IF 2.3 Q3 MANAGEMENT
Tianyang Wang , JamesS. Dyer , Warren J. Hahn
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引用次数: 5

摘要

许多重要的决策和风险分析问题由于输入变量之间的依赖关系而变得复杂。在这种情况下,标准的一次一个变量的灵敏度分析方法通常被避免,而倾向于完全概率的,或n-way的分析技术,同时改变所有n个输入变量并捕获它们的相互依赖性。不幸的是,单向敏感性分析提供的许多直觉可能无法在完全概率方法中获得,因为很难或不可能分离出单个变量的边际效应。在本文中,我们提出了一种依赖调整的方法,通过使用基于copula的概率灵敏度分析来识别和分析模型中输入变量的影响。这种方法提供了关于输入变量的影响和输入变量之间的依赖关系的见解。这种方法的一个贡献是,它有助于评估变量的相对边际影响,以便在关注计算效率的应用中确定哪些变量应该建模,例如在大规模问题的决策树分析中。此外,我们还研究了模型对输入中相关性大小的敏感性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensitivity analysis of decision making under dependent uncertainties using copulas

Many important decision and risk analysis problems are complicated by dependencies between input variables. In such cases, standard one-variable-at-a-time sensitivity analysis methods are typically eschewed in favor of fully probabilistic, or n-way, analysis techniques which simultaneously vary all n input variables and capture their interdependencies. Unfortunately, much of the intuition provided by one-way sensitivity analysis may not be available in fully probabilistic methods because it is difficult or impossible to isolate the marginal effects of the individual variables. In this paper, we present a dependence-adjusted approach for identifying and analyzing the impact of the input variables in a model through the use of probabilistic sensitivity analysis based on copulas. This approach provides insights about the influence of the input variables and the dependence relationships between the input variables. One contribution of this approach is that it facilitates assessment of the relative marginal influence of variables for the purpose of determining which variables should be modeled in applications where computational efficiency is a concern, such as in decision tree analysis of large-scale problems. In addition, we also investigate the sensitivity of a model to the magnitude of correlations in the inputs.

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来源期刊
CiteScore
2.70
自引率
10.00%
发文量
15
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