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Activity Details

84 Mon, 8/9/2021, 10:00 AM - 11:50 AM Virtual
Advances in Spatio-Temporal Statistics with Applications to Environmental Data — Topic-Contributed Papers
Section on Statistics and the Environment, Section on Statistical Computing
Organizer(s): Marcin Jurek, University of Texas
Chair(s): Mitchell Krock, Rutgers University
10:05 AM A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Data Sets
Jaewoo Park, Yonsei University; Ben Seiyon Lee, George Mason University
10:25 AM 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
10:45 AM Deep Neural Network Estimation for Complex Spatial Processes
Amanda Lenzi, Argonne National Laboratory
11:05 AM 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
11:25 AM Statistical Issues in Uncertainty Quantification for Satellite-Based Carbon Flux Inversion
Michael Stanley, Carnegie Mellon University; Mikael Kuusela, Carnegie Mellon University
11:45 AM Floor Discussion