Environmetrics. 2007 Nov 05;19(5):487-507. doi: 10.1002/env.891.

A class of nonseparable and nonstationary spatial temporal covariance functions.

Environmetrics

Montserrat Fuentes, Li Chen, Jerry M Davis

PMID: 19829763 PMCID: PMC2761043 DOI: 10.1002/env.891

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).

References

1. J Am Stat Assoc. 2007;102(480):1221-1234 - PubMed

Publication Types

• Journal Article

Grant support

• R01 ES014843/ES/NIEHS NIH HHS/United States
• R01 ES014843-01A2/ES/NIEHS NIH HHS/United States
• R01 ES014843-02/ES/NIEHS NIH HHS/United States