Design of Experiment in Global Sensitivity Analysis Based on ANOVA High-Dimensional Model Representation

被引：14

作者：

Wang, Xiaodi ^{[1
]}

Tang, Yincai ^{[1
]}

Chen, Xueping ^{[2
]}

Zhang, Yingshan ^{[1
]}

机构：

[1] E China Normal Univ, Sch Finance & Stat, Shanghai 200241, Peoples R China

[2] Jiangsu Teachers Univ Technol, Dept Math, Changzhou, Peoples R China

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2010年 / 39卷 / 06期

关键词：

Design of experiment; Global sensitivity analysis; Global sensitivity indices; Monte Carlo algorithm; Orthogonal decomposition;

D O I：

10.1080/03610918.2010.484122

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This article reviews global sensitivity analysis based on ANOVA high-dimensional model representation. To overcome the computational difficulties and explore the use of design of experiment (DOE) in global sensitivity analysis, two methods are presented. If the form of the objective function f is known, we use DOE to estimate the global sensitivity indices instead of Monte Carlo simulation. Otherwise, we use the observed values of the experiments to do global sensitivity analysis. These methods are easy to implement and can reduce the computational cost. An example is given to show the feasibility of replacing Monte Carlo (MC) or quasi-Monte Carlo (quasi-MC) simulation by design of experiment.

引用

页码：1183 / 1195

页数：13

共 50 条

[1] Symmetrical Design of Experiment in Global Sensitivity Analysis Based on ANOVA High-dimensional Model Representation
Luo, Chun
Zhang, Yingshan
[J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2016, 45 (01) : 48 - 72
[2] Orthogonal Arrays for the Estimation of Global Sensitivity Indices Based on ANOVA High-Dimensional Model Representation
Wang, Xiaodi
Tang, Yincai
Zhang, Yingshan
[J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2011, 40 (09) : 1324 - 1341
[3] Global sensitivity analysis for the Rothermel model based on high-dimensional model representation
Liu, Yaning
Hussaini, Yousuff
Oekten, Giray
[J]. CANADIAN JOURNAL OF FOREST RESEARCH, 2015, 45 (11) : 1474 - 1479
[4] Orthogonal arrays for estimating global sensitivity indices of non-parametric models based on ANOVA high-dimensional model representation
Wang, Xiaodi
Tang, Yincai
Zhang, Yingshan
[J]. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2012, 142 (07) : 1801 - 1810
[5] High-dimensional model representation-based global sensitivity analysis and the design of a novel thermal management system for lithium-ion batteries
Huang, Hongfei
Wang, Hu
Gu, Jinqing
Wu, Yunqiang
[J]. ENERGY CONVERSION AND MANAGEMENT, 2019, 190 : 54 - 72
[6] High-Dimensional Data Global Sensitivity Analysis Based on Deep Soft Sensor Model
Yi, Ling
Ding, Jinliang
Liu, Changxin
Chai, Tianyou
[J]. IEEE TRANSACTIONS ON CYBERNETICS, 2023, 53 (09) : 5741 - 5754
[7] Global sensitivity analysis based on high-dimensional sparse surrogate construction
Hu, Jun
Zhang, Shudao
[J]. APPLIED MATHEMATICS AND MECHANICS-ENGLISH EDITION, 2017, 38 (06) : 797 - 814
[8] Global sensitivity analysis based on high-dimensional sparse surrogate construction
Jun HU
Shudao ZHANG
[J]. Applied Mathematics and Mechanics(English Edition), 2017, 38 (06) : 797 - 814
[9] Global sensitivity analysis based on high-dimensional sparse surrogate construction
Jun Hu
Shudao Zhang
[J]. Applied Mathematics and Mechanics, 2017, 38 : 797 - 814
[10] High-dimensional model representation for structural reliability analysis
Chowdhury, Rajib
Rao, B. N.
Prasad, A. Meher
[J]. COMMUNICATIONS IN NUMERICAL METHODS IN ENGINEERING, 2009, 25 (04): : 301 - 337

← 1 2 3 4 5 →