An Analytic Method for Evaluating the Performance of Aggregation Rules for Probability Densities

Stephen C. Hora ()
Additional contact information
Stephen C. Hora: Viterbi School of Engineering, and School of Policy, Planning, and Development, University of Southern California, Los Angeles, California 90089

Operations Research, 2010, vol. 58, issue 5, 1440-1449

Abstract: It is shown how infinite sequences of densities with defined properties can be used to evaluate the expected performance of mathematical aggregation rules for elicited densities. The performance of these rules is measured through the average variance, calibration, and average Brier score of the aggregates. A general result for the calibration of the arithmetic average of densities from well-calibrated independent experts is given. Arithmetic and geometric aggregation rules are compared using sequences of normal densities. Sequences are developed that exhibit dependence among experts and lack of calibration. The impact of correlation, number of experts, and degree of calibration on the performance of the aggregation is demonstrated.

Keywords: linear opinion pools; geometric average; expert judgment; subjective probability; scoring rules; expert combination; dependence; overconfidence; consensus probability; calibration; elicitation; Brier score (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.1100.0789 (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:oropre:v:58:y:2010:i:5:p:1440-1449

Access Statistics for this article

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