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Quantile-Parameterized Distributions for Expert Knowledge Elicitation

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Title: Quantile-Parameterized Distributions for Expert Knowledge Elicitation
Authors: Perepolkin, Dmytro, Lindström, Erik, Sahlin, Ullrika
Contributors: Lund University, Faculty of Science, Centre for Mathematical Sciences, Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Originator, Lund University, Faculty of Science, Centre for Mathematical Sciences, Mathematical Statistics, Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Matematisk statistik, Originator, Lund University, Faculty of Science, Centre for Environmental and Climate Science (CEC), Lunds universitet, Naturvetenskapliga fakulteten, Centrum för miljö- och klimatvetenskap (CEC), Originator
Source: Decision Analysis. 22(3):169-188
Subject Terms: Natural Sciences, Mathematical Sciences, Probability Theory and Statistics, Naturvetenskap, Matematik, Sannolikhetsteori och statistik
Description: 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.
Access URL: https://doi.org/10.1287/deca.2024.0219
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  Data: Quantile-Parameterized Distributions for Expert Knowledge Elicitation
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  Data: <searchLink fieldCode="AR" term="%22Perepolkin%2C+Dmytro%22">Perepolkin, Dmytro</searchLink><br /><searchLink fieldCode="AR" term="%22Lindström%2C+Erik%22">Lindström, Erik</searchLink><br /><searchLink fieldCode="AR" term="%22Sahlin%2C+Ullrika%22">Sahlin, Ullrika</searchLink>
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  Data: Lund University, Faculty of Science, Centre for Mathematical Sciences, Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Originator<br />Lund University, Faculty of Science, Centre for Mathematical Sciences, Mathematical Statistics, Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Matematisk statistik, Originator<br />Lund University, Faculty of Science, Centre for Environmental and Climate Science (CEC), Lunds universitet, Naturvetenskapliga fakulteten, Centrum för miljö- och klimatvetenskap (CEC), Originator
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  Data: <i>Decision Analysis</i>. 22(3):169-188
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  Data: 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.
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