Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data

Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda and Yulong Wang

Papers from arXiv.org

Abstract: 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.

Date: 2022-04, Revised 2023-05
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Journal Article: Tuning parameter-free nonparametric density estimation from tabulated summary data (2024)
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