Matern Cross-Covariance Functions for Multivariate Random Fields

• 成果类型: Article
• 署名作者: Gneiting, Tilmann; Kleiber, William; Schlather, Martin
• 署名单位: Ruprecht Karls University Heidelberg; University of Washington; University of Washington Seattle; University of Gottingen
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2010.tm09420
• 发表日期: 2010
• 页码: 1167-1177
• 关键词: linear coregionalization model; maximum-likelihood-estimation; asymptotic properties; bayesian-analysis; SPACE; CONSTRUCTION; prediction

摘要

We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spatial random fields, where each constituent component is a Matern process. The model parameters are interpretable in terms of process variance, smoothness, correlation length, and colocated correlation coefficients, which can be positive or negative. Both the marginal and the cross-covariance functions are of the Matern type. In a data example on error fields for numerical predictions of surface pressure and temperature over the North American Pacific Northwest, we compare the bivariate Matern model to the traditional linear model of coregionalization.