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.
The Journal of the American Statistical Association (JASA) has long been considered the premier journal of statistical science. Science Citation Index reported JASA was the most highly cited journal in the mathematical sciences in 1991-2001, with 16,457 citations, more than 50% more than the next most highly cited journals. Articles in JASA focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences and on new methods of statistical education.
Building on two centuries' experience, Taylor & Francis has grown rapidlyover the last two decades to become a leading international academic publisher.The Group publishes over 800 journals and over 1,800 new books each year, coveringa wide variety of subject areas and incorporating the journal imprints of Routledge,Carfax, Spon Press, Psychology Press, Martin Dunitz, and Taylor & Francis.Taylor & Francis is fully committed to the publication and dissemination of scholarly information of the highest quality, and today this remains the primary goal.
This item is part of JSTOR collection
For terms and use, please refer to our Terms and Conditions
Journal of the American Statistical Association
© 2002 American Statistical Association
Request Permissions
