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Activity Number:
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414
- ENVR Student Paper Award Winners
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #322847
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Title:
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Bag of DAGs: Flexible Nonstationary Modeling of Spatiotemporal Dependence
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Author(s):
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Bora Jin* and Michele Peruzzi and David Dunson
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Companies:
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Duke university and Duke University and Duke University
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Keywords:
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Air pollution;
Bayesian geostatistics;
Directed acyclic graphs;
Gaussian process;
Particulate matter;
Scalability
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Abstract:
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We propose a class of nonstationary processes that characterize varying directional associations in space and time for point-referenced data. Our construction is based on local mixtures of sparse directed acyclic graphs (DAGs). In stochastically choosing DAG edges from a "bag," we account for uncertainty in directional correlation patterns across a domain. The resulting "bag of DAGs" processes (BAGs) lead to interpretable nonstationarity and scalability for large data due to sparsity of all DAGs in the bag. We are motivated by spatiotemporal modeling of air pollutants in which a directed edge in a DAG represents a prevailing wind direction causing some associated covariance in the pollutants. We outline Bayesian hierarchical models embedding the resulting nonstationary BAGs and illustrate inferential and performance gains of our methods compared to existing alternatives. We analyze fine particulate matter (PM2.5) in California with high-resolution data from low-cost air quality sensors. The code for all analyses is publicly available on GitHub.
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Authors who are presenting talks have a * after their name.
