Distribution-Free Statistical Inference with Lorenz Curves and Income Shares
推导了洛伦兹曲线纵坐标向量的渐近正态分布的完整方差-协方差结构，该结构仅依赖于条件一阶和二阶矩，可在不预设总体密度的情况下一致估计，从而将洛伦兹曲线和收入份额从描述性统计提升为统计推断工具。
The paper considers the problem of statistical inference with estimated Lorenz curves and income shares. The full variance-covariance structure of the (asymptotic) normal distribution of a vector of Lorenz curve ordinates is derived and shown to depend only on conditional first and second moments that can be estimated consistently without prior specification of the population density underlying the sample data. Lorenz curves and income shares can thus be used as tools for statistical inference instead of simply as descriptive statistics.