Tuning parameter-free nonparametric density estimation from tabulated summary data

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.

Automated summary

Administrative data, often presented as tabulated summaries for confidentiality reasons, can be more easily accessed in this form. In this study, the authors propose a novel nonparametric density estimation method based on maximum entropy and demonstrate its consistent results. The method does not require tuning parameters and provides a closed-form density for further analysis. The authors apply this method to estimate the income distribution using tabulated summary data from U.S. tax returns.