Nonstationary modeling for multivariate spatial processes

JOURNAL OF MULTIVARIATE ANALYSIS

卷:112

Article

Kleiber, William¹  Nychka, Douglas¹ 

[1] Natl Ctr Atmospher Res, Inst Math Appl Geosci, Geophys Stat Project, Boulder, CO 80307 USA

关键词: Cross-covariance;    Kernel smoother;    Local stationarity;    Matern;    Multivariate;    Nonstationary;    Spatial Gaussian process;   

DOI  :  10.1016/j.jmva.2012.05.011

来源: Elsevier

【 摘 要 】

We derive a class of matrix valued covariance functions where the direct and cross-covariance functions are Matern. The parameters of the Matern class are allowed to vary with location, yielding local variances, local ranges, local geometric anisotropies and local smoothnesses. We discuss inclusion of a nonconstant cross-correlation coefficient and a valid approximation. Estimation utilizes kernel smoothed empirical covariance matrices and a locally weighted minimum Frobenius distance that yields local parameter estimates at any location. We derive the asymptotic mean squared error of our kernel smoother and discuss the case when multiple field realizations are available. Finally, the model is illustrated on two datasets, one a synthetic bivariate one-dimensional spatial process, and the second a set of temperature and precipitation model output from a regional climate model. (C) 2012 Elsevier Inc. All rights reserved.

【 授权许可】

Free