论文标题
从列表的摘要数据中调整无参数的非参数密度估计
Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data
论文作者
论文摘要
由于保密性问题，通常比原始格式更容易作为表格摘要访问。在这种实际特征的促进下，我们提出了一种基于最大熵的摘要数据，提出了一种新型的非参数密度估计方法，并证明了其强大的均匀一致性。与现有的基于内核的估计器不同，我们的估计器不含调谐参数，并接受封闭形式的密度，该密度方便估计分析。我们将建议的方法应用于美国纳税申报表的列表摘要数据，以估算收入分配。
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.
