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Quantile-Parameterized Distributions for Expert Knowledge Elicitation

Author

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  • 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)

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.

Suggested Citation

  • Dmytro Perepolkin & Erik Lindström & Ullrika Sahlin, 2025. "Quantile-Parameterized Distributions for Expert Knowledge Elicitation," Decision Analysis, INFORMS, vol. 22(3), pages 169-188, September.
    • Handle: RePEc:inm:ordeca:v:22:y:2025:i:3:p:169-188
    DOI: 10.1287/deca.2024.0219
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