Uncertainty in prior elicitations: a nonparametric approach

• 成果类型: Article
• 署名作者: Oakley, Jeremy E.; O'Hagan, Anthony
• 署名单位: University of Sheffield
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asm031
• 发表日期: 2007
• 页码: 427441
• 关键词: DISTRIBUTIONS; inference

摘要

A key task in the elicitation of expert knowledge is to construct a distribution from the. finite, and usually small, number of statements that have been elicited from the expert. These statements typically specify some quantiles or moments of the distribution. Such statements are not enough to identify the expert's probability distribution uniquely, and the usual approach is to fit some member of a convenient parametric family. There are two clear deficiencies in this solution. First, the expert's beliefs are forced to fit the parametric family. Secondly, no account is then taken of the many other possible distributions that might have fitted the elicited statements equally well. We present a nonparametric approach which tackles both of these deficiencies. We also consider the issue of the imprecision in the elicited probability judgements.