Tuning parameter-free nonparametric density estimation from tabulated summary data
提出一种基于最大熵的非参数密度估计方法，适用于表格汇总数据，无需调参且具有强一致收敛性，可用于美国税收数据估计收入分布。
Administrative data are often easier to access as tabulated summaries than in the original format due to confidentiality concerns. Motivated by this practical feature, we propose a novel nonparametric density estimation method from tabulated summary data based on maximum entropy and prove its strong uniform consistency. Unlike existing kernel-based estimators, our estimator is free from tuning parameters and admits a closed-form density that is convenient for post-estimation analysis. We apply the proposed method to the tabulated summary data of the U.S. tax returns to estimate the income distribution.