Regression Quantiles and Global Measures of Robustness

回归分位数和全局稳健性度量

• 批准号: 8922472
• 负责人: Stephen Portnoy
• 金额: $ 14.83万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-05-15 至 1992-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8922472&HistoricalAwards=false
• 关键词: Regression; Quantiles; Global; Measures; Robustness

Economists are constantly in search of more robust statistical techniques so that departures from standard assumptions will not disrupt the estimation process. Regression quantile methods and "breakdown" and "tail-behavior" measures, the foci of this research project, hold a great deal of promise for developing more robust alternatives. The specific objectives of the project with respect to regression quantiles are to refine the current methods and to develop user friendly algorithms and programs for applying these methods in linear models and related areas, further extending the theory to include dependent and non-stationary cases, to study dual regression quantiles and their corresponding rank statistics and to develop large dimension asymptotics where there are a large number of parameters. A second line of research involves global measures of robustness and their relation to outlier diagnostics. Analyses will be completed to understand more completely what causes statistical procedures to break down in the presence of outliers, particularly through the exploration of the close connection between finite-sample "breakdown" and "tail behavior" of estimators, and to refine current procedures for detecting and adjusting for outliers and to develop inference methods that are highly resistant to the presence of outliers and influential observations. The development and distribution of the algorithms created by this project will hasten the use of these important methodological developments by applied researchers in economics. Extending the theoretical work will eventually result in better empirical work, as the departures from underlying assumptions like normality and independence will no longer necessarily lead to a breakdown in esimtation techniques.

经济学家一直在寻找更可靠的统计数据， 技术，使偏离标准假设不会 扰乱了评估过程。回归分位数方法和 “崩溃”和“尾部行为”的措施，这一焦点 研究项目，具有很大的发展前景 更强大的替代品。 项目在回归方面的具体目标 分位数是为了完善现有的方法，并开发用户 友好的算法和程序应用这些方法， 线性模型和相关领域，进一步扩展了理论 包括非独立和非平稳的情况下，研究双 回归分位数及其相应的秩统计量， 为了发展大尺寸渐近性， 参数数目。 第二条研究路线涉及全球 稳健性的度量及其与离群值诊断的关系。 将完成分析，以更全面地了解 导致统计程序在以下情况下崩溃： 离群值，特别是通过探索接近 有限样本“崩溃”和“尾部行为”之间的联系 的估计，并完善目前的程序， 调整离群值，并开发推理方法， 对异常值的存在具有高度抵抗力， 意见。 开发和分发的算法创建的 该项目将加速使用这些重要的 经济学应用研究者的方法论发展。 扩展理论工作将最终导致更好的 经验工作，因为偏离基本假设 像正常和独立将不再必然导致 导致了移植技术的崩溃