Quantile-Parameterized Distributions for Expert Knowledge Elicitation

Dmytro Perepolkin (), Erik Lindström () and Ullrika Sahlin ()
Additional contact information
Dmytro Perepolkin: Centre for Mathematical Sciences, Lund University, 223 62 Lund, Sweden
Erik Lindström: Centre for Mathematical Sciences, Lund University, 223 62 Lund, Sweden
Ullrika Sahlin: Centre for Environmental and Climate Science, Lund University, 223 62 Lund, Sweden

Decision Analysis, 2025, vol. 22, issue 3, 169-188

Abstract: This paper provides a comprehensive overview of quantile-parameterized distributions (QPDs) as a tool for capturing expert predictions and parametric judgments. We survey a range of methods for constructing distributions that are parameterized by a set of quantile-probability pairs and describe an approach to generalizing them to enhance their tail flexibility. Furthermore, we explore the extension of QPDs to the multivariate setting, surveying the approaches to construct bivariate distributions, which can be adopted to obtain distributions with quantile-parameterized margins. Through this review and synthesis of the previously proposed methods, we aim to enhance the understanding and utilization of QPDs in various domains.

Keywords: quantile functions; quantile-parameterized distributions; expert knowledge elicitation; Bayesian analysis (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/deca.2024.0219 (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:ordeca:v:22:y:2025:i:3:p:169-188

Access Statistics for this article

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