Combining Expert Opinions in Prior Elicitation
Combining Expert Opinions in Prior Elicitation
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DOI：
10.1214/12-ba717
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发表时间：
2012-01-01
影响因子：
4.4
通讯作者：
Rousseau, Judith
中科院分区：
文献类型：
--
作者：
Albert, Isabelle;Donnet, Sophie;Rousseau, Judith
我们考虑以明确的基于模型的方式结合不同专家的意见的问题，以在贝叶斯统计方法中构建有效的主观先验。我们提出了一种通用方法，考虑采用分层模型来解释各种变异来源以及专家之间的潜在依赖性。我们将这种方法应用于两个问题。第一个问题涉及食品风险评估问题，涉及对小鼠单核细胞增生李斯特菌污染的剂量反应建模。由于使用间接概率回归，在复杂的数学情况下考虑了两个层次的变异水平（专家之间和专家内部）。第二个问题涉及博士生在特定学校提交论文所需的时间。它说明了一种复杂的情况，其中对三个层次的变异进行了建模，但具有更简单的基础概率分布（对数正态）。
We consider the problem of combining opinions from different experts in an explicitly model-based way to construct a valid subjective prior in a Bayesian statistical approach. We propose a generic approach by considering a hierarchical model accounting for various sources of variation as well as accounting for potential dependence between experts. We apply this approach to two problems. The first problem deals with a food risk assessment problem involving modelling dose-response for Listeria monocytogenes contamination of mice. Two hierarchical levels of variation are considered (between and within experts) with a complex mathematical situation due to the use of an indirect probit regression. The second concerns the time taken by PhD students to submit their thesis in a particular school. It illustrates a complex situation where three hierarchical levels of variation are modelled but with a simpler underlying probability distribution (log-Normal).