论文标题
贝叶斯的排名和选择，可用于现场研究，经济流动性和预测
Bayesian ranking and selection with applications to field studies, economic mobility, and forecasting
论文作者
论文摘要
决策通常涉及排名和选择。例如，要组建一个政治预测者团队，我们可能首先将我们的选择范围缩小到候选人，我们充满信心地排名前10％的预测能力。不幸的是，我们不知道每个候选人的真实能力，而是观察到它的嘈杂估计。本文开发了新的贝叶斯算法，以根据嘈杂的估计值对候选人进行排名和选择。使用基于经验数据的模拟，我们表明我们的算法通常优于频繁的排名和选择算法。我们的贝叶斯排名算法在保持大致正确的覆盖范围的同时，会产生较短的等级置信区间。我们的贝叶斯选择算法在保持正确的错误率的同时选择更多的候选者。我们将排名和选择程序应用于现场实验，经济流动性，预测和类似问题。最后，我们在此处记录的用户友好的Python软件包中实现了排名和选择技术：https：//dsbowen-conditional-inperion.readthedocs.io/en/latest/。
Decision-making often involves ranking and selection. For example, to assemble a team of political forecasters, we might begin by narrowing our choice set to the candidates we are confident rank among the top 10% in forecasting ability. Unfortunately, we do not know each candidate's true ability but observe a noisy estimate of it. This paper develops new Bayesian algorithms to rank and select candidates based on noisy estimates. Using simulations based on empirical data, we show that our algorithms often outperform frequentist ranking and selection algorithms. Our Bayesian ranking algorithms yield shorter rank confidence intervals while maintaining approximately correct coverage. Our Bayesian selection algorithms select more candidates while maintaining correct error rates. We apply our ranking and selection procedures to field experiments, economic mobility, forecasting, and similar problems. Finally, we implement our ranking and selection techniques in a user-friendly Python package documented here: https://dsbowen-conditional-inference.readthedocs.io/en/latest/.
