Nonseparable, stationary covariance functions for space-time data

• 成果类型: Article
• 署名作者: Gneiting, T
• 署名单位: University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214502760047113
• 发表日期: 2002
• 页码: 590-600
• 关键词: population-density; ozone exposure; harris county; texas

摘要

Geostatistical approaches to spatiotemporal prediction in environmental science, climatology, meteorology. and related fields rely on appropriate covariance models. This article proposes general classes of nonseparable, stationary covariance functions for spatiotemporal random processes, The constructions are directly in the space-time domain and do not depend on closed-form Fourier inversions. The model parameters can be associated with the data's spatial and temporal structures, respectively: and a covariance model with a readily interpretable space-time interaction parameter is fitted to wind data from Ireland.