论文标题
统一的统计学习模型，用于排名和分数，并具有应用程序授予小组审查
A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review
论文作者
论文摘要
排名和分数是法官使用的两种常见数据类型，以表达对象集合中质量的偏好和/或感知。存在许多模型来分别研究每种类型的数据，但是没有统一的统计模型同时捕获两种数据类型，而无需先执行数据转换。我们建议通过共享参数结合了槌槌的$ ϕ $排名模型与二项式得分模型的共享参数，这些参数量化了对象质量，共识排名和法官之间的共识水平，我们建议将木棍丁基模型与二项式得分模型结合在一起。我们提出了一种有效的树搜索算法来计算模型参数的精确MLE，在分析和模拟上研究模型的统计特性，并将我们的模型应用于收集分数和部分排名的赠款面板审查的实例中的真实数据。此外，我们演示了如何使用模型输出来置信度对象进行排名。提出的模型被证明可以明智地结合分数和排名的信息，以量化对象质量并以适当水平的统计不确定性来衡量共识。
Rankings and scores are two common data types used by judges to express preferences and/or perceptions of quality in a collection of objects. Numerous models exist to study data of each type separately, but no unified statistical model captures both data types simultaneously without first performing data conversion. We propose the Mallows-Binomial model to close this gap, which combines a Mallows' $ϕ$ ranking model with Binomial score models through shared parameters that quantify object quality, a consensus ranking, and the level of consensus between judges. We propose an efficient tree-search algorithm to calculate the exact MLE of model parameters, study statistical properties of the model both analytically and through simulation, and apply our model to real data from an instance of grant panel review that collected both scores and partial rankings. Furthermore, we demonstrate how model outputs can be used to rank objects with confidence. The proposed model is shown to sensibly combine information from both scores and rankings to quantify object quality and measure consensus with appropriate levels of statistical uncertainty.
