Bayesian Quantile Matching Estimation
This addresses data access limitations for researchers in fields like medical diagnostics or policy advice, though it is incremental as it builds on existing Bayesian and quantile estimation techniques.
The authors tackled the problem of learning from aggregated data, such as quantiles, due to privacy constraints, by proposing a Bayesian method that accurately reflects uncertainty in empirical quantiles, and they demonstrated its application through simulations and real-world examples with a provided Python package.
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.