A new class of spatial temporal covariances for nonseparability and nonstationarity

M Fuentes, L Chen, J Davis

Research output: Contribution to journalArticle (Academic Journal)peer-review

44 Citations (Scopus)

Abstract

Spectral methods are powerful tools to study and model the dependency structure of spatial temporal processes. However, standard spectral approaches as well as geostatistical methods assume separability and stationarity of the covariance function; these can be very unrealistic assumptions in many settings. In this work, we introduce a general and flexible parametric class of spatial temporal covariance models, that allows for lack of stationarity and separability by using a spectral representation of the process. This new class of covariance models has a unique parameter that indicates the strength of the interaction between the spatial and temporal components; it has the separable covariance model as a particular case. We introduce an application with ambient ozone air pollution data provided by the U.S. Environmental Protection Agency (U.S. EPA).
Translated title of the contributionA new class of spatial temporal covariances for nonseparability and nonstationarity
Original languageEnglish
Pages (from-to)487 - 507
Number of pages21
JournalEnvironmetrics
Volume19, issue 5
DOIs
Publication statusPublished - Aug 2008

Bibliographical note

Publisher: Wiley

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