Further results on orthogonal arrays for the estimation of global sensitivity indices based on alias matrix

Xue-ping Chen, Jin-Guan Lin (), Xiao-di Wang and Xing-fang Huang

Statistical Methods & Applications, 2015, vol. 24, issue 3, 426 pages

Abstract: In this paper, the use of orthogonal arrays with strength $$s>p,$$ s > p , where $$p$$ p is the required strength, for global sensitivity analysis is considered. We first generalize the alias matrix for ANOVA high-dimensional model representation based on matrix image, and then by sequentially minimizing the squared alias degrees, we present a approach for the estimation of sensitivity indices. A two-level orthogonal array with 16 runs and a four-level orthogonal array with 64 runs are studied for estimating both low-order and high-order significant sensitivity indices. Moreover, models containing larger than 10 input factors are also investigated. All cases show that designs with smaller squared alias degree have less bias and variance for the estimations of global sensitivity indices. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Alias matrix; Matrix image; ANOVA HDMR; Strength; Global sensitivity indices; 62K05 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s10260-014-0290-7 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:24:y:2015:i:3:p:411-426

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-014-0290-7

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().