Paper Info
Title
Vine Copula-Based Parametric Sensitivity Analysis Of Failure Probability-Based Importance Measure In The Presence Of Multidimensional Dependencies
Abstract
In this paper, we propose derivative-oriented parametric sensitivity indices to investigate the influence of parameter uncertainty on a previously proposed failure probability-based importance measure in the presence of multidimensional dependencies. Herein, the vine copula function, a powerful mathematical tool for modeling variable dependencies, is utilized to establish the joint probability density function (PDF) for multidimensional dependencies. Based on the properties of the copula function, the developed parametric sensitivity indices are decomposed into independent and dependent parts. Using these parts, different types of contributions to the failure probability are identified. By computing the kernel function for each marginal PDF and the copula kernel function for each pair-copula PDF involved in the vine factorization, a general numerical algorithm is developed for estimating separated parametric sensitivity indices. Finally, the feasibility of the proposed indices and numerical solutions is verified through a numerical example and by solving two engineering problems.
Year
DOI
Venue
2021
10.1016/j.ress.2021.107898
RELIABILITY ENGINEERING & SYSTEM SAFETY
Keywords
DocType
Volume
Sensitivity analysis, Importance measure, Vine copula, Multidimensional correlation, Decomposition
Journal
215
ISSN
Citations 
PageRank 
0951-8320
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Haihe Li100.68
Pan Wang221.45
Xiaoyu Huang300.68
Zheng Zhang400.68
Changcong Zhou501.35
Zhufeng Yue6154.40