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Quantile-Parameterized Distributions for Expert Knowledge Elicitation

Perepolkin, Dmytro LU orcid ; Lindström, Erik LU orcid and Sahlin, Ullrika LU orcid (2025) In Decision Analysis 22(3). p.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.
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Decision Analysis
volume
22
issue
3
pages
20 pages
publisher
INFORMS Institute for Operations Research and the Management Sciences
ISSN
1545-8490
DOI
10.1287/deca.2024.0219
language
English
LU publication?
yes
id
c0becbff-4b95-4173-9ccf-df8e9ddde8fb
alternative location
https://pubsonline.informs.org/doi/10.1287/deca.2024.0219
date added to LUP
2025-10-06 18:06:44
date last changed
2025-10-13 17:07:14
@article{c0becbff-4b95-4173-9ccf-df8e9ddde8fb,
  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.}},
  author       = {{Perepolkin, Dmytro and Lindström, Erik and Sahlin, Ullrika}},
  issn         = {{1545-8490}},
  language     = {{eng}},
  month        = {{09}},
  number       = {{3}},
  pages        = {{169--188}},
  publisher    = {{INFORMS Institute for Operations Research and the Management Sciences}},
  series       = {{Decision Analysis}},
  title        = {{Quantile-Parameterized Distributions for Expert Knowledge Elicitation}},
  url          = {{http://dx.doi.org/10.1287/deca.2024.0219}},
  doi          = {{10.1287/deca.2024.0219}},
  volume       = {{22}},
  year         = {{2025}},
}