US EPA

2336775 

Journal Article 

A class of covariate- dependent spatiotemporal covariance functions for the analysis of daily ozone concentration 

Reich, BJ; Eidsvik, Jo; Guindani, M; Nail, A; Schmidt, AM 

2011 

Yes 

Annals of Applied Statistics
ISSN: 1932-6157 

5 

4 

2425-2447 

English 

24772199 

10.1214/11-AOAS482 

WOS:000300382800009 

In geostatistics, it is common to model spatially distributed phenomena through an underlying stationary and isotropic spatial process. However, these assumptions are often untenable in practice because of the influence of local effects in the correlation structure. Therefore, it has been of prolonged interest in the literature to provide flexible and effective ways to model non-stationarity in the spatial effects. Arguably, due to the local nature of the problem, we might envision that the correlation structure would be highly dependent on local characteristics of the domain of study, namely the latitude, longitude and altitude of the observation sites, as well as other locally defined covariate information. In this work, we provide a flexible and computationally feasible way for allowing the correlation structure of the underlying processes to depend on local covariate information. We discuss the properties of the induced covariance functions and discuss methods to assess its dependence on local covariate information by means of a simulation study and the analysis of data observed at ozone-monitoring stations in the Southeast United States. 

Covariance estimation; nonstationarity; ozone; spatial data analysis