Robust Statistics for Correlated Data

相关数据的稳健统计

• 批准号: 0204297
• 负责人: Leonard Stefanski
• 金额: $ 17.92万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-01 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0204297&HistoricalAwards=false
• 关键词: Robust; Statistics; Correlated; Data

AbstractDMS-0204297PI: M. GentonTitle: Robust Statistics For Correlated DataThis research focuses on two main new ideas. First, the concept of breakdown point is generalized to correlated observations by defining the critical region of the badness measure implicitly rather than explicitly, thus covering situations with both uncorrelated and correlated observations. Secondly, a new method to robustify likelihoods is introduced that can be applied in any situation where a parametric likelihood is available. This new approach is intimately connected to measurement error models and reduces to well-known contaminated normal likelihoods in location-scale independent, identically distributed settings. This new approach will be used to robustify many of the commonly used methods in spatial data settings.The applications of this research include real data sources from environmental sciences, namely ozone concentrations and fine particulate matter in the U.S. This work will have impact on both, theoretical statisticians and practitioners of spatio-temporal statistics, and hence will foster collaboration between academia and industry. It aims to provide more reliable analyses of spatio-temporal data sets, and thus contribute to a better understanding of physical processes governing atmospherically-transported pollutants. In particular, it will provide more reliable evaluations of the effectiveness of the legislated emission reductions mandated in the Clean Air Act Amendments of 1990. Moreover, this project will support the teaching and promotion of robust statistics and spatio-temporal statistics at North Carolina State University.

摘要dms - 0204297pi: M. GentonTitle：稳健统计相关数据本研究主要关注两个新思想。首先，通过隐式而非显式地定义不良度量的临界区域，将击穿点的概念推广到相关观测值，从而涵盖了不相关和相关观测值的情况。其次，介绍了一种新的鲁棒似然方法，该方法可以应用于任何可用参数似然的情况。这种新方法与测量误差模型密切相关，并在位置尺度独立、相同分布的环境中降低了众所周知的污染正态似然。这种新方法将用于增强空间数据设置中的许多常用方法。这项研究的应用包括来自环境科学的真实数据来源，即美国的臭氧浓度和细颗粒物。这项工作将对理论统计学家和时空统计实践者产生影响，因此将促进学术界和工业界之间的合作。它旨在提供更可靠的时空数据集分析，从而有助于更好地理解控制大气输送污染物的物理过程。特别是，它将对1990年《清洁空气法修正案》规定的立法减排的有效性提供更可靠的评估。此外，该项目将支持北卡罗莱纳州立大学稳健统计和时空统计的教学和推广。