Resampling methods for spatial regression models under a class of stochastic designs

• 成果类型: Article
• 署名作者: Lahiri, S. N.; Zhu, Jun
• 署名单位: Iowa State University; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053606000000551
• 发表日期: 2006
• 页码: 1774-1813
• 关键词: mixing conditions; M-ESTIMATORS; bootstrap; variance; statistics; prediction

摘要

In this paper we consider the problem of bootstrapping a class of spatial regression models when the sampling sites are generated by a (possibly nonuniform) stochastic design and are irregularly spaced. It is shown that the natural extension of the existing block bootstrap methods for grid spatial data does not work for irregularly spaced spatial data under nonuniform stochastic designs. A variant of the blocking mechanism is proposed. It is shown that the proposed block bootstrap method provides a valid approximation to the distribution of a class of M-estimators; of the spatial regression parameters. Finite sample properties of the method are investigated through a moderately large simulation study and a real data example is given to illustrate the methodology.