Nonparametric methods for comparing distribution functionals for dependent samples with application to inequality measures
依赖样本下分布泛函比较的非参数方法及不平等应用AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接比较不平等、贫困等分布泛函并分析家庭金融不平等，支持分布方向分类，风险指标的提及不足以归入系统性风险。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
依赖样本下分布泛函比较的非参数方法及不平等应用
- 研究问题
- 比较不平等、贫困或风险等分布指标时，如何处理两个样本之间的任意依赖或观测配对重叠，并构造可靠的置信区间？
- 方法与思路
- 作者分别提出适用于任意样本间依赖的交集方法及重叠样本方法，结合渐近与自助推断，并用影响函数估计渐近方差。
- 材料陈述的贡献
- 摘要称重叠样本区间在模拟中具有较满意的覆盖率和精度，任意依赖下交集方法亦可靠，并用于分析意大利家庭金融不平等变化。
- 阅读关注点
- 应核对任意依赖与仅由匹配对产生依赖的区别、交集区间构造、影响函数条件，以及重尾分布下方差和自助法的有效性。
- 阅读边界
- 所据为 arXiv 预印本，同行评审及期刊接收未经核实；摘要指出重尾可能削弱渐近推断，自助法在方差很大或不存在时也有局限。
原文摘要
This paper proposes asymptotically distribution-free inference methods for comparing a broad range of welfare indices across dependent samples, including those employed in inequality, poverty, and risk analysis. Two distinct situations are considered. \emph{First}, we propose asymptotic and bootstrap intersection methods which are completely robust to arbitrary dependence between two samples. \emph{Second}, we focus on the common case of overlapping samples -- a special form of dependent samples where sample dependence arises solely from matched pairs -- and provide asymptotic and bootstrap methods for comparing indices. We derive consistent estimates for asymptotic variances using the influence function approach. The performance of the proposed methods is studied in a simulation experiment: we find that confidence intervals with overlapping samples exhibit satisfactory coverage rates with reasonable precision, whereas conventional methods based on an assumption of independent samples have an inferior performance in terms of coverage rates and interval widths. Asymptotic inference can be less reliable when dealing with heavy-tailed distributions, while the bootstrap method provides a viable remedy, unless the variance is substantial or nonexistent. The intersection method yields reliable results with arbitrary dependent samples, including instances where overlapping samples are not feasible. We demonstrate the practical applicability of our proposed methods in analyzing dynamic changes in household financial inequality in Italy over time.