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Activity Number:
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9
- When Location Is Random: Advances in Statistical Modeling and Inference for Spatial Point Processes
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Type:
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Invited
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Date/Time:
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Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #321902
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Title:
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Multivariate Spatial Point Patterns via Correlated Bernoulli Indicators
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Author(s):
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Matthew Heaton* and Matthew Bekker
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Companies:
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Brigham Young University and Brigham Young University
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Keywords:
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Latent variable ;
Bayesian model ;
Spatial correlation
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Abstract:
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Various tree species exhibit unique environmental adaptations that allow them to survive in different microclimatic conditions. In order to understand environmental barriers to species-specific establishment, and how edaphic features influence them, the Forest Inventory and Analysis (FIA) program of the United States Department of Agriculture (USDA) collects location and environmental information regarding various tree species throughout the Interior West. Here, we propose a multivariate Bernoulli model to understand the spatial distribution of multiple species of pine through the Interior West and how each species is influenced by its surrounding environment.
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Authors who are presenting talks have a * after their name.
