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359
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Wed, 8/5/2020,
10:00 AM -
2:00 PM
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Virtual
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Advances in Spatial and Spatio-Temporal Statistics — Contributed Papers
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Section on Statistics and the Environment
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Chair(s): Erin Schliep, University of Missouri
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Lagrangian Spatio-Temporal Nonstationary Covariance Functions
Mary Lai Salvana, KAUST; Marc Genton, KAUST
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DeepKriging: A Spatially Dependent Deep Neural Networks for Spatial Prediction
Yuxiao Li, King Abdullah University of Science and Technology; Ying Sun, King Abdullah University of Science and Technology (KAUST); Brian Reich, North Carolina State University
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Uncertainty Quantification and Inference for Spatio-Temporal Forecasting via Echo State Mixture Density Networks with Relevance Propagation
Ranadeep Daw, University of Missouri; Christopher Wikle, University of Missouri
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Gaussian Processes and Boosting: An Impossible Marriage?
Fabio Sigrist
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Hierarchical Sparse Cholesky Decomposition with Applications to High-Dimensional Spatio-Temporal Filtering
Marcin Jurek, Texas A & M University; Matthias Katzfuss, Texas A&M University
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A Spectral Method for Non-Gridded Univariate and Multivariate Spatial Data Using Monte Carlo Integration
Matthew Miller, North Carolina State University; Brian Reich, North Carolina State University
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Spatial Statistical Methods to Assess the Relationship Between Water Violations and Poverty at the County Level: In America, Who Has Access to Clean Water?
Ruby Bayliss, Drexel University; Loni Tabb, Drexel University
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Multilevel Indicator Kriging for Non-Gaussian Random Fields
Gaurav Agarwal, King Abdullah University of Science and Technology; Ying Sun, King Abdullah University of Science and Technology (KAUST)
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