Grantee Research Project Results
1999 Progress Report: Hierarchical Statistical Analysis of Global and Regional Environmental Data
EPA Grant Number: R827257Title: Hierarchical Statistical Analysis of Global and Regional Environmental Data
Investigators: Cressie, Noel A.C. , Berliner, Mark , Wikle, Christopher K.
Current Investigators: Cressie, Noel A.C. , Berliner, Mark , Wilke, Christopher K.
Institution: Iowa State University
Current Institution: The Ohio State University , University of Missouri - Kansas City
EPA Project Officer: Hahn, Intaek
Project Period: September 1, 1998 through August 31, 2001 (Extended to March 31, 2003)
Project Period Covered by this Report: September 1, 1998 through August 31, 1999
Project Amount: $325,000
RFA: Environmental Statistics (1998) RFA Text | Recipients Lists
Research Category: Aquatic Ecosystems , Environmental Statistics , Human Health
Objective:
The first objective is to implement hierarchical spatio-temporal statistical modeling of environmental phenomena (e.g., sea-surface temperatures, disease mortality/morbidity rates, remote-sensing data). The second objective is to find computationally efficient ways to filter, statistically, massive spatio-temporal data sets, such as one obtains from polar-orbiting satellites. The third objective is to investigate ways to link two or more spatio-temporal environmental processes, one being explanatory for the other (e.g., meteorology might be explanatory for wetland ecology).
Progress Summary:
The problem of mapping disease mortality rates is addressed in Cressie, Stern, and Wright (1999) and Stern and Cressie (1999). Maps of raw rates, disease counts divided by total populations at risk, have been criticized as unreliable due to nonconstant variance associated with heterogeneity in base population sizes. Spatial analysis of data of this sort can be handled very naturally through Bayesian hierarchical statistical modeling, where there is a measurement process at the first level, an explanatory process at the second level, and prior probability distribution on unknowns at the third level. Then disease maps can be created using Bayes or empirical Bayes point estimates. Uncertainty in these estimates can be expressed through an ensemble of maps from the posterior distribution. The use of posterior predictive model checks for assessing model fitness in this setting was proposed by Stern and Cressie (1999). In particular, the cross-validation posterior predictive distributions, obtained by reanalyzing the data without a suspect small area, proved to be particularly powerful.
Spatial statistical models are applied in many problems for which dependence in observed random variables is not easily explained by a direct scientific mechanism. The paper by Kaiser, Cressie, and Lee (1999) explores a latent spatial process that can be modeled in the form of a spatial mixing distribution. Methods for the specification of flexible joint mixing distributions through multi-parameter exponential family conditional distributions are presented. A forest-health data set obtained under the Environmental Monitoring and Assessment Program (EMAP) was fit by Monte Carlo maximum likelihood.
The paper by Zhu, Lahiri, and Cressie (1999) establishes a functional central limit theorem for the empirical predictor of a spatial cumulative distribution function when the underlying random field has a nonstationary mean structure. In this case, the spatial asymptotics are a combination of "increasing domain" and "infill".
The article by Lahiri, Lee, and Cressie (1999) considers the least squares approach for estimating parameters of a spatial variogram and establishes consistency and asymptotic normality of these estimators under general conditions. Two necessary and sufficient conditions for these estimators to be asymptotically efficient are provided.
Future Activities:
In the future, spatial cumulative distribution functions, which provide a statistical summary of random processes over a spatial domain of interest, will be compared at two time points. Also, the principal of temporally dynamic hierarchical models will be used to investigate various topics. One topic is the spreading of an infectious disease via contact between infected and susceptible individuals. Another is long-lead prediction of tropical Pacific sea-surface temperatures. A third is the assessment of anthropogenic impacts on climate. Hierarchical spatial statistical models will be used to provide optimal change-of-resolution filtering of massive environmental data sets from polar-orbiting satellites. Also, the problem of approximating ecological bias will be addressed.
Journal Articles on this Report : 3 Displayed | Download in RIS Format
| Other project views: | All 103 publications | 29 publications in selected types | All 19 journal articles |
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Cressie N, Stern HS, Wright DR. Mapping rates associated with polygons. Journal of Geographical Systems 2000;2(1):61-69. |
R827257 (1999) R827257 (Final) |
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Lahiri SN, Lee Y, Cressie N. On asymptotic distribution and asymptotic efficiency of least squares estimators of spatial variogram parameters. Journal of Statistical Planning and Inference 2002;103(1-2):65-85. |
R827257 (1999) R827257 (2001) R827257 (Final) |
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Stern HS, Cressie N. Posterior predictive model checks for disease mapping models. Statistics in Medicine 2000;19(17-18):2377-2397. |
R827257 (1999) R827257 (Final) |
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Supplemental Keywords:
ambient air, atmosphere, global climate, stratospheric ozone, precipitation, health effects, ecological effects, human health, Bayesian, biology, ecology, epidemiology, mathematics, modeling, climate models, satellite, remote sensing, agriculture., RFA, Air, Scientific Discipline, Ecosystem Protection/Environmental Exposure & Risk, Economic, Social, & Behavioral Science Research Program, Environmental Statistics, Mathematics, Ecology, Ecosystem/Assessment/Indicators, Ecological Effects - Environmental Exposure & Risk, air toxics, climate change, Bayesian space-time model, regional environmental data, EMAP, satellite data, mortality rates, stratospheric ozone, Global Climate Change, risk assessment, global warming, environmental monitoring, statistical models, climate models, statistical methods, hierarchical statistical analysis, remote sensing, global environmental data, EOSRelevant Websites:
Program in Spatial Statistics and Environmental Sciences (SSES) Web site:
http://www.stat.ohio-state.edu/~sses
SSES Program, U.S. Environmental Protection Agency
(this grant) Web site:
http://www.stat.ohio-state.edu/~sses/research_epa.html
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.