Covariance Structure Models and Influence Diagrams

William J. Burns and Robert T. Clemen
Additional contact information
William J. Burns: University of Iowa, Iowa City, Iowa 52242
Robert T. Clemen: University of Oregon, Eugene, Oregon 97403

Management Science, 1993, vol. 39, issue 7, 816-834

Abstract: Statisticians use covariance structure modeling as a versatile tool for modeling and testing theory. The models that result provide explicit and detailed descriptions of stochastic systems. We show how covariance structure models are related---mathematically, conceptually, philosophically and practically---to Gaussian influence diagrams as described by Shachter and Kenley (1989). This relationship suggests ways in which covariance structure modeling can be used to advantage in the prescriptive domain of decision analysis. The paper includes an example concerning the management of hazardous materials, in which a covariance structure model is converted to an influence diagram for use in a prescriptive analysis.

Keywords: causal model; covariance structure models; influence diagrams; multivariate normal distributions; risk analysis; decision analysis (search for similar items in EconPapers)
Date: 1993
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.39.7.816 (application/pdf)

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:inm:ormnsc:v:39:y:1993:i:7:p:816-834

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().