Assessing the Statistical Quality of Eigenvector Spatial Filter-Based Estimators

评估基于特征向量空间滤波器的估计器的统计质量

• 批准号: 1229223
• 负责人: Yongwan Chun
• 金额: $ 11.5万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1229223&HistoricalAwards=false
• 关键词: Assessing; Statistical; Quality; Eigenvector; Spatial

This project investigates the qualities of eigenvector spatial filtering (ESF) methodology in a linear regression context. Although ESF has become more popular as a technique for addressing spatial autocorrelation latent in georeferenced data, the quality of ESF-based estimators for regression models has not been thoroughly investigated. The statistical qualities of ESF-based estimators needing assessment include unbiasedness, efficiency, consistency, and robustness. Such a quality assessment of ESF-based estimators can bolster the efficacy of ESF methodology, documenting that it furnishes a solid foundation for analyzing georeferenced data with linear regression. Multiple testing corrections in ESF-based estimation is another area that has not been adequately investigated. The will help fill this gap in the literature and advance the utility of eigenvector spatial filtering for linear regression.This project will advance knowledge and understanding of ESF, which is a recently developed method for statistical modeling of georeferenced data. The assessment of unbiasedness, efficiency, consistency, and robustness is essential to determine the usability of the ESF-based estimators. This project will furnish sound fundamentals of the ESF methodology for linear regression techniques. This will permit ESF methodology to be extended to modeling non-normal georeferenced data. Project results will facilitate research capabilities across the range of fields that have adopted ESF methodology, including economics, regional science, epidemiology, and ecology.

本计画探讨线性回归背景下的特征向量空间滤波方法的品质。 虽然ESF已经成为一种更流行的技术，用于解决空间自相关潜在的地理参考数据，回归模型的基于ESF的估计的质量还没有得到彻底的研究。 需要评估的基于ESF的估计的统计质量包括无偏性，效率，一致性和鲁棒性。 这种基于ESF的估计量的质量评估可以增强ESF方法的有效性，证明它为使用线性回归分析地理参考数据奠定了坚实的基础。 基于ESF的估计中的多重检验校正是另一个尚未充分研究的领域。 这将有助于填补文献中的这一空白，并推进线性回归的特征向量空间滤波的实用性。该项目将促进对ESF的认识和理解，ESF是最近开发的地理参考数据统计建模方法。 无偏性，效率，一致性和鲁棒性的评估是必不可少的，以确定基于ESF的估计的可用性。 这个项目将为线性回归技术提供ESF方法的良好基础。 这将允许ESF方法扩展到非正常地理参考数据建模。 项目成果将促进采用ESF方法的一系列领域的研究能力，包括经济学，区域科学，流行病学和生态学。