A Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures.
Journal
Risks
Type
journal article
Date Issued
2021-05-28
Author(s)
Abstract (De)
The key purpose of this paper is to present an alternative viewpoint for combining expert opinions based on finite mixture models. Moreover, we consider that the components of the mixture are not necessarily assumed to be from the same parametric family. This approach can enable the agent to make informed decisions about the uncertain quantity of interest in a flexible manner that accounts for multiple sources of heterogeneity involved in the opinions expressed by the experts in terms of the parametric family, the parameters of each component density, and also the mixing weights. Finally, the proposed models are employed for numerically computing quantile-based risk measures in a collective decision-making context.
Language
English
Keywords
opinion pooling
finite mixture models
expectation maximization algorithm
quantile-based risk measures
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
MDPI
Division(s)
Contact Email Address
despoina.makariou@unisg.ch
Eprints ID
268233
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A Finite Mixture Modelling Perspective for Combining Experts’ Opinions.pdf
Size
634.42 KB
Format
Adobe PDF
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