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Activity Number: 386 - Filtering Methods for Spatio-Temporal Big Data Applications
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #300051
Title: Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models
Author(s): Matthias Katzfuss and Christopher K. Wikle and Jonathan R Stroud*
Companies: Texas A & M University and University of Missouri and Georgetown University
Keywords: data assimilation; geoscience applications; Gibbs sampler; particle filter; spatio-temporal statistics; state-space models

We propose a new class of filtering and smoothing methods for inference in high-dimensional, nonlinear, non-Gaussian, spatio-temporal state-space models. The main idea is to combine the ensemble Kalman filter and smoother, developed in the geophysics literature, with state-space algorithms from the statistics literature. Our algorithms address a variety of estimation scenarios, including on-line and off-line state and parameter estimation. We take a Bayesian perspective, for which the goal is to generate samples from the joint posterior distribution of states and parameters. The key benefit of our approach is the use of ensemble Kalman methods for dimension reduction, which allows inference for high-dimensional state vectors. We compare our methods to existing ones, including ensemble Kalman filters, particle filters, and particle MCMC. Using a real data example of cloud motion and data simulated under a number of nonlinear and non-Gaussian scenarios, we show that our approaches outperform these existing methods.

Authors who are presenting talks have a * after their name.

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