Description:

Major goals of environmental data analysis are the assessment of the condition of the environment and the relationship between condition and potential stressors. Environmental stress occurs at different temporal and spatial scales.

URLs/Downloads:

2000 Progress Report

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Record Details:

Record Type:PROJECT( ABSTRACT )

Start Date:10/01/1999

Completion Date:09/30/2002

Record ID: 52261

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Related Organizations:

Role :OWNER

Organization Name :VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY

Citation :Blacksburg

State :VA

Zip Code :24061

Project Information:

Approach :Methods used to relate measures of environmental health and environmental information are typically based on regression or correlation analysis and include methods such as multiple regression analysis, canonical correlation analysis and canonical correspondence analysis. Typical data sets are large in terms of the number of variables used in the modeling process and it may be quite difficult to select amongst different competing models. This research will investigate the use of Bayesian analysis as a set of methods for interpreting the importance of relationships between stressors and environmental variables and for prediction under this model uncertainty. The approaches that will be used is based on Bayesian model averaging and Bayesian hierarchical modeling. The research will use multiple regression analysis to evaluate relationships between biological measures of health and chemical, physical, habitat and land use variables. Bayesian model averaging will be used to assess variable importance and to make inferences and predictions. Models based on canonical correspondence analysis and canonical discriminant analysis are commonly used in the assessment of environmental data. The research will also extend Bayesian model averaging to applications involving canonical correlation analysis, canonical correspondence analysis and canonical discriminant analysis.

Cost :$266,388.00

Research Component :Environmental Statistics

Project IDs:

ID Code :R827953

Project type :EPA Grant