Andrade, JAA and Gosling, JP orcid.org/0000-0002-4072-3022 (2018) Expert knowledge elicitation using item response theory. Journal of Applied Statistics, 45 (6). pp. 2981-2998. ISSN 0266-4763
Abstract
In an expert knowledge elicitation exercise, experts face a carefully constructed list of questions that they answer according to their knowledge. The elicitation process concludes when a probability distribution is found that adequately captures the experts' beliefs in the light of those answers. In many situations, it is very difficult to create a set of questions that will efficiently capture the experts' knowledge, since experts might not be able to make precise probabilistic statements about the parameter of interest. We present an approach for capturing expert knowledge based on item response theory, in which a set of binary response questions is proposed to the expert, trying to capture responses directly related to the quantity of interest. As a result, the posterior distribution of the parameter of interest will represent the elicited prior distribution that does not assume any particular parametric form. The method is illustrated by a simulated example and by an application involving the elicitation of rain prophets' predictions for the rainy season in the north-east of Brazil.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 16 March 2018, available online: https://doi.org/10.1080/02664763.2018.1450365 |
Keywords: | Subjective probability; item response theory; latent trait; prior information; nonparametric elicitation; rain prophets |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Mar 2018 12:23 |
Last Modified: | 17 Mar 2019 01:38 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/02664763.2018.1450365 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:128337 |