文章基本信息

标题：A Quantitative Study of Quantile Based Direct Prior Elicitation from Expert Opinion
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作者：Dipak K. Dey ; Junfeng Liu
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2007
卷号：2
期号：1
页码：137-166
出版社：International Society for Bayesian Analysis
摘要：Eliciting priors from expert opinion enjoys more eciency and re- liability by avoiding the statistician's potential subjectivity. Since elicitation on the predictive prior probability space requires too-simple priors and may be bur- dened with additional uncertainties arising from the response model, quantitative elicitation of exible priors on the direct prior probability space deserves much attention. Motivated by precisely acquiring the shape information for the gen- eral location-scale-shape family beyond the limited and simple location-scale fam- ily, we investigate multiple numerical procedures for a broad class of priors, as well as interactive graphical protocols for more complicated priors. We highlight the quantile based approaches from several aspects, where Taylor's expansion is demonstrated to be an ecient approximate alternative to work on the regions in which the shape parameter is highly sensitive. By observing inherent associa- tions between the scale and shape parameters, we put more weight on practical solutions under a proper sensitivity index (SI) rather than presumability. Our proposed methodology is demonstrated through skew-normal and Gamma hyper- parameter elicitation where the shape parameter is numerically solved in a stable way. The performance comparisons among di erent elicitation approaches are also provided.
关键词：location parameter, prior elicitation, quantile, scale parameter, shape parameter, skewness, Taylor's expansion