Reliable inference for the Gini index
基尼系数的可靠推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究基尼系数及贫困指数，直接涉及分布与不平等测度的推断。
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基尼系数的可靠推断
- 研究问题
- 如何为基尼系数的代入估计量构造计算简单、表现可靠的标准误和偏差校正，并处理厚尾分布带来的推断困难？
- 方法与思路
- 作者将估计量近似表示为独立同分布随机变量之和，据此推导标准误与偏差校正，并使用自助法缓解厚尾问题。
- 材料陈述的贡献
- 摘要称近似推断在多数模拟中表现良好，自助法可缓解部分厚尾问题，类似方法还可用于若干非线性贫困指数。
- 阅读关注点
- 应核对近似表示的余项、所需矩条件与自助法方案，并重点查看厚尾、极大方差或方差不存在时的失效边界。
- 阅读边界
- 仅据所给出版社摘要；“可靠”来自所述模拟表现，摘要已指出厚尾限制，不能据此保证任意收入分布下有效。
原文摘要
Although attention has been given to obtaining reliable standard errors for the plug-in estimator of the Gini index, all standard errors suggested until now are either complicated or quite unreliable. An approximation is derived for the estimator by which it is expressed as a sum of IID random variables. This approximation allows us to develop a reliable standard error that is simple to compute. A simple but effective bias correction is also derived. The quality of inference based on the approximation is checked in a number of simulation experiments, and is found to be very good unless the tail of the underlying distribution is heavy. Bootstrap methods are presented which alleviate this problem except in cases in which the variance is very large or fails to exist. Similar methods can be used to find reliable standard errors of other indices which are not simply linear functionals of the distribution function, such as Sen's poverty index and its modification known as the Sen-Shorrocks-Thon index.