Spatial modeling with system of stochastic partial differential equations. (March 2016)

• Record Type: Journal Article
• Authors: Hu, Xiangping; Steinsland, Ingelin
• Is Part Of: Wiley interdisciplinary reviews. Volume 8:Number 2(2016)
• Page Start: 112
• Page End: 125
• Publication Date: 2016-03
• Subjects: Stochastic partial differential equations -- Gaussian random fields -- Spatial models -- Covariance function; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1378
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract : To define a spatial process as the solution to a stochastic partial differential equation (SPDE) is an approach to spatial modeling that is gaining popularity. The model corresponds to a Gaussian random spatial process with Matérn covariance function. The SPDE approach allows for computational benefits and provides a framework for making valid complex models (e.g., nonstationary spatial models). Using systems of SPDEs to define spatial processes extends the class of models that can be specified as SPDEs, while the computational benefits are kept. In this study, we give an overview of the current state of spatial modeling with systems of SPDEs. Systems of SPDEs have contributed toward modeling and computational efficient inference for spatial Gaussian random field (GRF) models with oscillating covariance functions and multivariate GRF models. For multivariate GRF models special systems of SPDEs corresponding to models known from the literature are set up. Little work has been done for exploring opportunities and properties of spatial processes defined as systems of SPDEs. We also describe some of the interesting topics for further research. WIREs Comput Stat 2016, 8:112–125. doi: 10.1002/wics.1378 For further resources related to this article, please visit theWIREs website .