Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data

• Tác giả: Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda, Yulong Wang
• Ngôn ngữ: eng
• Ký hiệu phân loại: 003.75 Nonlinear systems
• Thông tin xuất bản: 2022
• Bộ sưu tập: Metadata
• ID: 194908

Tóm tắt

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.