Cross-classified hierarchical Bayesian models for risk-based analysis of complex systems under sparse data

Zhenyu Yan and Yacov Y. Haimes

Reliability Engineering and System Safety, 2010, vol. 95, issue 7, 764-776

Abstract: Decisionmaking problems in risk analysis often involve extreme events, where empirical data are usually either sparse or lacking. With sparse data, important parameters and quantities for risk and safety analyses may not be estimated and tested within an acceptable level of significance. This paper applies Hierarchical Bayesian Models (HBMs) to reduce the estimation variance and thus build relatively robust models for extreme event data through borrowing strength from different but related systems or subsystems. Based on this application, this paper further applied HBMs with cross-classified random effects (CHBMs) to address the multi-dimensional property of complex systems and borrow strength from the multiple dimensions of such systems. Case studies with both simulated and real data demonstrate the effectiveness of HBMs and CHBMs in risk-based systems analysis.

Keywords: Risk analysis; Sparse data; Hierarchical Bayesian model; Strength borrowing; Complex system; System of systems (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832010000505
Full text for ScienceDirect subscribers only

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:eee:reensy:v:95:y:2010:i:7:p:764-776

DOI: 10.1016/j.ress.2010.02.014

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().