论文标题
贝叶斯分位数匹配估计
Bayesian Quantile Matching Estimation
论文作者
论文摘要
由于对数据保护的认识提高和相应的法律，许多数据，尤其是涉及敏感个人信息的数据，无法公开访问。因此，许多数据收集机构仅释放汇总数据，例如提供人口分布的平均分位数。然而，研究和科学理解，例如对于医疗诊断或政策建议，通常依靠数据访问。为了克服这一张力，我们提出了一种从分位数信息中学习的贝叶斯方法。根据有限样本的顺序统计，我们的方法充分正确地反映了经验分位数的不确定性。在概述了理论之后，我们将方法应用于模拟和现实世界的示例。此外，我们提供了一个基于Python的软件包，该软件包实现了所提出的模型。
Due to increased awareness of data protection and corresponding laws many data, especially involving sensitive personal information, are not publicly accessible. Accordingly, many data collecting agencies only release aggregated data, e.g. providing the mean and selected quantiles of population distributions. Yet, research and scientific understanding, e.g. for medical diagnostics or policy advice, often relies on data access. To overcome this tension, we propose a Bayesian method for learning from quantile information. Being based on order statistics of finite samples our method adequately and correctly reflects the uncertainty of empirical quantiles. After outlining the theory, we apply our method to simulated as well as real world examples. In addition, we provide a python-based package that implements the proposed model.
