Nonseparable, Stationary Covariance Functions for Space-Time Data

Author: Gneiting T.¹

Source: Journal of the American Statistical Association, Volume 97, Number 458, 1 June 2002 , pp. 590-600(11)

Publisher: American Statistical Association

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Abstract:

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.

Keywords: COMPLETELY MONOTONE; CORRELATION FUNCTION; GEOSTATISTICS; KRIGING; POSITIVE DEFINITE; SEPARABLE; SPATIOTEMPORAL

Document Type: Research article

Affiliations: 1: Department of Statistics, University of Washington, Seattle, WA 98195-4322

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