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.
