Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models

• 成果类型: Article
• 署名作者: Katzfuss, Matthias; Stroud, Jonathan R.; Wikle, Christopher K.
• 署名单位: Texas A&M University System; Texas A&M University College Station; Georgetown University; University of Missouri System; University of Missouri Columbia
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1592753
• 发表日期: 2020
• 页码: 866-885
• 关键词: sequential monte-carlo; error covariance-matrix; Data assimilation; parameter-estimation; scale mixtures; joint state; filter; likelihood; CONVERGENCE; posterior

摘要

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 online 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. for this article are available online.