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Activity Details

131 Mon, 8/9/2021, 1:30 PM - 3:20 PM Virtual
Methods for Spatial, Temporal, and Spatio-Temporal Data — Contributed Speed
Section on Statistics and the Environment
Chair(s): Brooke Alhanti, Duke University
1:35 PM Nonstationary Nearest Neighbor Gaussian Process: Hierarchical Model Architecture and MCMC Sampling
Sébastien Coube-Sisqueille, Université de Pau et des Pays de l'Adour; Sudipto Banerjee, University of California Los Angeles ; Benoît Liquet, Université de Pau et des Pays de l'Adour
1:40 PM Computational Developments and Applications of the Multi-Resolution Approximation for Massive Spatial Data
Lewis Blake, Colorado School of Mines; Huang Huang, King Abdullah University of Science and Technology; Matthias Katzfuss, Texas A&M University; Dorit Hammerling, Colorado School of Mines
1:45 PM Fast Correlation-Based Sparse Inverse Cholesky Factorization for Gaussian Processes
Myeongjong Kang, Texas A&M University; Matthias Katzfuss, Texas A&M University
1:50 PM Bag of DAGs: Flexible and Scalable Modeling of Spatiotemporal Dependence
Bora Jin, Duke University; Michele Peruzzi, Duke University; James Johndrow, University of Pennsylvania; David Dunson, Duke University
1:55 PM A Semiparametric Approach for Prediction with Large Geostatistical Data Sets
Joshua French, University of Colorado Denver; Mohammad Meysami, University of Colorado Denver
2:00 PM Regionalized Models with Spatially Continuous Predictions at the Borders
Jadon Wagstaff, Utah State University; Brennan Bean, Utah State University
2:05 PM Ordered Conditional Approximation of Potts Models
Anirban Chakraborty, Texas A&M University; Matthias Katzfuss, Texas A&M University
2:10 PM Response Envelopes for Multivariate Spatial Data
Hossein Moradi Rekabdarkolaee, South Dakota State University
2:15 PM Continuous-Time Discrete-Space Movement Models Over Two- and Three-Dimensional Space
Joshua Hewitt, Duke University; Robert S Schick, Duke University; Alan E Gelfand, Duke University
2:20 PM Extensions of Dynamic Factor Analysis for Better Modeling of Noisy Ecological Time Series
Eric Ward, NOAA; Sean Anderson, Fisheries and Oceans Canada; Mary Hunsicker, NOAA; Mike Litzow, NOAA
2:30 PM Transformed-Linear Prediction for Extremes
Jeongjin Lee, Colorado State University; Daniel Cooley, Colorado State University
2:35 PM Modeling Short-Ranged Dependence in Block Extrema with Application to Polar Temperature Data
Brook Russell, Clemson University School of Mathematical and Statistical Sciences; Whitney Huang, Clemson University School of Mathematical and Statistical Sciences
2:40 PM Spatio-Temporal Analysis Frequency Separation and Block Bootstraps of Periodically Correlated Time Series
Edward L Valachovic, University at Albany, State University of New York
2:45 PM Spatial Analysis of Nonstationary Spatial Interval-Valued Data
Austin Workman, Baylor University; Joon Jin Song, Baylor University
2:50 PM Data-Driven Approach to Nonlinear Dynamic Equation Discovery
Joshua S North, University of Missouri; Erin M Schliep, University of Missouri; Christopher Wikle, University of Missouri
2:55 PM Modeling Spatial Dependence with Cauchy Convolution Processes
Pavel Krupskii, University of Melbourne; Raphaël Huser, KAUST
3:00 PM Nonlinear Spatial Prediction with Predictor Subject to Limit of Detection
Minho Kim, Baylor University; Kyuhee Shin, Kyungpook National University; Gyuwon Lee , Kyungpook National University; Joon Jin Song, Baylor University
3:05 PM A General Ensemble Filtering Framework Using Quantiles
Jeffrey Anderson, National Center for Atmospheric Research
3:10 PM Floor Discussion