Point and interval estimation of variogram models using spatial empirical likelihood

• 成果类型: Article
• 署名作者: Nordman, Daniel J.; Caragea, Petruta C.
• 署名单位: Iowa State University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214507000001391
• 发表日期: 2008
• 页码: 350-361
• 关键词: CONFIDENCE-REGIONS

摘要

We present a spatial blockwise empirical likelihood method for estimating variogram model parameters in the analysis of spatial data on a grid. The method produces point estimators that require no spatial variance estimates to compute, unlike least squares methods for variogram fitting, but are as efficient as the best least squares estimator in large samples. Our approach also produces confidence regions for the variogram, without requiring knowledge of the full joint distribution of the spatial data. In addition, the empirical likelihood formulation extends to spatial regression problems and allows simultaneous inference on both spatial trend and variogram parameters. We examine the asymptotic behavior of the estimator analytically, and investigate its behavior in finite samples through simulation studies.