Covariance structure of spatial and spatiotemporal processes. (23rd May 2013)

• Record Type: Journal Article
• Authors: Guttorp, Peter; Schmidt, Alexandra M.
• Is Part Of: Wiley interdisciplinary reviews. Volume 5:Number 4(2013)
• Page Start: 279
• Page End: 287
• Publication Date: 2013-05-23
• Subjects: convolution -- nonseparable -- nonstationary -- full symmetry; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1259
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract : An important aspect of statistical modeling of spatial or spatiotemporal data is to determine the covariance function. It is a key part of spatial prediction (kriging). The classical geostatistical approach uses an assumption of isotropy, which yields circular isocorrelation curves. However, this is inappropriate for many applications, and several nonstationary approaches have been developed. Adding the temporal aspect, there is often interaction between time and space, requiring classes of nonseparable covariance structures. WIREs Comput Stat 2013, 5:279–287. doi: 10.1002/wics.1259 This article is categorized under: Data: Types and Structure > Image and Spatial Data Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data