Nonseparable, Stationary Covariance Functions for Space–Time Data
- 1 June 2002
- journal article
- Published by Informa UK Limited in Journal of the American Statistical Association
- Vol. 97 (458), 590-600
- https://doi.org/10.1198/016214502760047113
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
This publication has 25 references indexed in Scilit:
- Product-sum covariance for space-time modeling: an environmental applicationEnvironmetrics, 2000
- Blur-generated non-separable space–time modelsJournal of the Royal Statistical Society Series B: Statistical Methodology, 2000
- On the physical geometry concept at the basis of space/time geostatistical hydrologyAdvances in Water Resources, 2000
- Classes of Nonseparable, Spatio-Temporal Stationary Covariance FunctionsJournal of the American Statistical Association, 1999
- Ozone Exposure and Population Density in Harris County, TexasJournal of the American Statistical Association, 1997
- Ozone Exposure and Population Density in Harris County, Texas: CommentJournal of the American Statistical Association, 1997
- The dynamics of error covariances in a barotropic modelTellus A: Dynamic Meteorology and Oceanography, 1993
- A Coordinate Transformation for Objective Frontal AnalysisMonthly Weather Review, 1993
- Bayesian Prediction of Deterministic Functions, with Applications to the Design and Analysis of Computer ExperimentsJournal of the American Statistical Association, 1991
- A simple spatial-temporal model of rainfallProceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 1988