Encoding Expert Opinion on Skewed Non-Negative Distributions
Often only limited information can be elicited from experts about a distribution, such as quantiles or other summary statistics. Skewed non-negative distributions often arise in practice, and present a particular challenge for elicitation due to their asymmetry. This paper provides a range of simple approaches to encoding these types of distributions. We consider the popular two-parameter gamma and lognormal distributions, as well as the three-parameter location-shifted lognormal and quantile-specified Davies distribution. Equations are provided for moment-matching approaches, each depending on a different though minimal set of summary statistics that have been elicited from experts. When additional information has been elicited, regression can be applied to these moment-matching equations. A simulation study and case study illustrate the varying accuracy that can be achieved, depending on the encoding method (which summary statistics are used), the distributional choice and the expert. More broadly this research emphasizes the need to question distributional choice when distributions, such as priors, are encoded using few summary statistics.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Journal Article|
|Additional Information:||Access to the author-version is currently restricted pending permission from the publisher. For more information, please refer to the journal's website (see hypertext link) or contact the author.|
|Keywords:||Expert elicitation, encoding, lognormal, quantiles|
|Subjects:||Australian and New Zealand Standard Research Classification > PSYCHOLOGY AND COGNITIVE SCIENCES (170000) > COGNITIVE SCIENCE (170200) > Knowledge Representation and Machine Learning (170203)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Statistics not elsewhere classified (010499)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2008 Dixie W Publishing Corporation|
|Deposited On:||20 Nov 2008 00:00|
|Last Modified:||02 Sep 2012 23:00|
Repository Staff Only: item control page