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84
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Mon, 8/9/2021,
10:00 AM -
11:50 AM
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Virtual
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Advances in Spatio-Temporal Statistics with Applications to Environmental Data — Topic-Contributed Papers
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Section on Statistics and the Environment, Section on Statistical Computing
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Organizer(s): Marcin Jurek, University of Texas
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Chair(s): Mitchell Krock, Rutgers University
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10:05 AM
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A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Data Sets
Jaewoo Park, Yonsei University; Ben Seiyon Lee, George Mason University
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10:25 AM
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A Negative Binomial Process Model of the 2020–2021 COVID-19 Epidemic in Rhode Island
Nathan Wikle, Pennsylvania State University; Ephraim Hanks, Penn State University; Maciej Boni, Penn State University
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10:45 AM
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Deep Neural Network Estimation for Complex Spatial Processes
Amanda Lenzi, Argonne National Laboratory
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11:05 AM
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Scalable Forward Sampler Backward Smoother Based on the Vecchia Approximation
Marcin Jurek, University of Texas; Matthias Katzfuss, Texas A&M University; Pulong Ma, Duke University / SAMSI
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11:25 AM
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Statistical Issues in Uncertainty Quantification for Satellite-Based Carbon Flux Inversion
Michael Stanley, Carnegie Mellon University; Mikael Kuusela, Carnegie Mellon University
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11:45 AM
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Floor Discussion
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