Nested symmetrical Latin hypercube designs

Xiaodi Wang and Hengzhen Huang ()
Additional contact information
Xiaodi Wang: Central University of Finance and Economics
Hengzhen Huang: Guangxi Normal University

Statistical Papers, 2024, vol. 65, issue 7, No 11, 4299-4330

Abstract: Abstract Symmetrical global sensitivity analysis (SGSA) can aid practitioners in reducing the model complexity by identifying symmetries within the model. In this paper, we propose a nested symmetrical Latin hypercube design (NSLHD) for implementing SGSA in a sequential manner. By combining the strengths of the nested Latin hypercube design and symmetrical design, the proposed design allows for the implementation of SGSA without the need to pre-determine the sample size of the experiment. We develop a random sampling procedure and an efficient sequential optimization algorithm to construct flexible NSLHDs in terms of runs and factors. Sampling properties of the constructed designs are studied. Numerical examples are given to demonstrate the effectiveness of the NSLHD for designing sequential sensitivity analysis.

Keywords: Design of experiment; Global sensitivity analysis; Latin hypercube design; Sequential evaluation; Symmetrical design (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00362-024-01556-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:stpapr:v:65:y:2024:i:7:d:10.1007_s00362-024-01556-y

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-024-01556-y

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().