期刊
SPATIAL STATISTICS
卷 14, 期 -, 页码 505-531出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.spasta.2015.10.001
关键词
Annual precipitation; Penalized maximum likelihood; Non-stationary spatial modelling; Stochastic partial differential equations; Gaussian random fields; Gaussian Markov random fields
A stationary spatial model is an idealization and we expect that the true dependence structures of physical phenomena are spatially varying, but how should we handle this non-stationarity in practice? Westudy the challenges involved in applying a flexible non-stationary model to a dataset of annual precipitation in the conterminous US, where exploratory data analysis shows strong evidence of a non-stationary covariance structure. The aim of this paper is to investigate the modelling pipeline once non-stationarity has been detected in spatial data. We show that there is a real danger of over-fitting the model and that careful modelling is necessary in order to properly account for varying second-order structure. In fact, the example shows that sometimes non-stationary Gaussian random fields are not necessary to model non-stationary spatial data. (C) 2015 Elsevier B.V. All rights reserved.
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