Journal article
Bayesian entropy for spatial sampling design of environmental data
Environmental and Ecological Statistics, Vol.14(3), pp.323-340
09/01/2007
DOI: 10.1007/s10651-007-0017-0
Abstract
Issue Title: Spatial and Spatio-temporal Modeling in Environmental and Ecological Statistics, Guest Editor: Alan Gelfand We develop a spatial statistical methodology to design national air pollution monitoring networks with good predictive capabilities while minimizing the cost of monitoring. The underlying complexity of atmospheric processes and the urgent need to give credible assessments of environmental risk create problems requiring new statistical methodologies to meet these challenges. In this work, we present a new method of ranking various subnetworks taking both the environmental cost and the statistical information into account. A Bayesian algorithm is introduced to obtain an optimal subnetwork using an entropy framework. The final network and accuracy of the spatial predictions is heavily dependent on the underlying model of spatial correlation. Usually the simplifying assumption of stationarity, in the sense that the spatial dependency structure does not change location, is made for spatial prediction. However, it is not uncommon to find spatial data that show strong signs of nonstationary behavior. We build upon an existing approach that creates a nonstationary covariance by a mixture of a family of stationary processes, and we propose a Bayesian method of estimating the associated parameters using the technique of Reversible Jump Markov Chain Monte Carlo. We apply these methods for spatial prediction and network design to ambient ozone data from a monitoring network in the eastern US.[PUBLICATION ABSTRACT]
Details
- Title: Subtitle
- Bayesian entropy for spatial sampling design of environmental data
- Creators
- Montserrat Fuentes - University of Iowa, Provost Office AdministrationArin ChaudhuriDavid M Holland
- Resource Type
- Journal article
- Publication Details
- Environmental and Ecological Statistics, Vol.14(3), pp.323-340
- Publisher
- Springer Nature B.V; Dordrecht
- DOI
- 10.1007/s10651-007-0017-0
- ISSN
- 1352-8505
- eISSN
- 1573-3009
- Language
- English
- Date published
- 09/01/2007
- Academic Unit
- Biostatistics; Provost Office Administration; Statistics and Actuarial Science
- Record Identifier
- 9983759098002771
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