Grantee Research Project Results
1999 Progress Report: Combining Environmental Data Using Hierarchical Bayesian Space-Time Models
EPA Grant Number: R826887Title: Combining Environmental Data Using Hierarchical Bayesian Space-Time Models
Investigators: Zimmerman, Dale , Cowles, Mary Kathryn
Institution: University of Iowa
EPA Project Officer: Hahn, Intaek
Project Period: January 1, 1999 through December 31, 2001
Project Period Covered by this Report: January 1, 1999 through December 31,1999
Project Amount: $199,904
RFA: Environmental Statistics (1998) RFA Text | Recipients Lists
Research Category:
Objective:
To develop general and flexible statistical models for combining temporal, spatial, and spatio-temporal data. The models and associated statistical methodology will allow users of environmental data to combine data from disparate sources when analyzing relationships between, for example, pollutants and effects endpoints.
Progress Summary:
The specific objectives of the grant are listed below along with a summary of the aspects that have been accomplished to date.
- To develop Bayesian hierarchical models for time-series, spatial, and spatio-temporal processes that combine data from disparate sources.
We have developed and implemented Bayesian hierarchical models for time-series and geostatistical processes that combine data from different sources.
- To develop efficient Markov Chain Monte Carlo (MCMC) algorithms for implementing such models, and to assess their convergence.
We have developed two efficient MCMC algorithms for Bayesian geostatistical models. Furthermore, we have developed a simulation method for verifying theoretical convergence bounds for MCMC samplers for a class of models that includes intrinsic autoregressions for time-series and spatial data.
- To evaluate model performance using simulation studies.
We have carried out small simulation studies to evaluate the performance of our Bayesian models and computing algorithms for time-series and geostatistical data.
- To demonstrate gains in efficiency and predictive ability using the combined data rather than any of its component parts individually.
Future Activities:
Having successfully developed models for data that are temporally correlated only or spatially correlated only, and having successfully fit these models to important environmental datasets, we will now turn our attention to space-time data (i.e., data that are temporally and spatially correlated). We will formulate Bayesian hierarchical models for such data and fit them by Markov Chain Monte Carlo techniques. We will continue our work on computational strategies for assessing and accelerating convergence of MCMC samplers.
Journal Articles on this Report : 3 Displayed | Download in RIS Format
Other project views: | All 15 publications | 6 publications in selected types | All 6 journal articles |
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Type | Citation | ||
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Cowles MK. MCMC sampler convergence rates for hierarchical normal linear models: A simulation approach. Statistics and Computing 2002;12(4):377-389. |
R826887 (1999) R826887 (2000) |
not available |
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Isaacson JD, Zimmerman DL. Combining temporally correlated environmental data from two measurement systems. Journal of Agricultural, Biological, and Environmental Statistics 2000;5(4):398-416. |
R826887 (1999) R826887 (2000) |
Exit |
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Zimmerman DL, Holland DM. Complementary co-kriging: spatial prediction using data combined from several environmental monitoring networks. Environmetrics 2005;16(3),219-234. |
R826887 (1999) |
not available |
Supplemental Keywords:
global climate, acid rain, monitoring, RFA, Economic, Social, & Behavioral Science Research Program, Ecosystem Protection/Environmental Exposure & Risk, Ecosystem/Assessment/Indicators, Ecological Effects - Environmental Exposure & Risk, Environmental Statistics, risk assessment, hierarchical Bayesian space-time models, Bayesian space-time model, co-pollutant effects, global environmental data, prediction, simulation studies, streamflow, statistical models, combined data, data analysis, spatial-temporal methods, data models, global warming, innovative statistical modelsRelevant Websites:
Relevant Web Sites:
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.