Journal article
A class of nonseparable and nonstationary spatial temporal covariance functions
Environmetrics (London, Ont.), Vol.19(5), pp.487-507
11/05/2007
DOI: 10.1002/env.891
PMCID: PMC2761043
PMID: 19829763
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).
Details
- Title: Subtitle
- A class of nonseparable and nonstationary spatial temporal covariance functions
- Creators
- Montserrat Fuentes - Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, U.S.ALi Chen - Center for Integrating Statistical and Environmental Science (CISES), University of Chicago, Chicago, IL 60637, U.S.AJerry M Davis - Department of Marine Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695-8203, U.S.A
- Resource Type
- Journal article
- Publication Details
- Environmetrics (London, Ont.), Vol.19(5), pp.487-507
- DOI
- 10.1002/env.891
- PMID
- 19829763
- PMCID
- PMC2761043
- NLM abbreviation
- Environmetrics
- ISSN
- 1180-4009
- eISSN
- 1099-095X
- Language
- English
- Date published
- 11/05/2007
- Academic Unit
- Statistics and Actuarial Science; President; Biostatistics
- Record Identifier
- 9984065775902771
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