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Activity Number: 308 - Recent Advancements in Spatial and Spatio-Temporal Modeling
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #305205
Title: Modeling Multivariate Spatial Processes with Applications in Remote Sensing
Author(s): Emily Lei Kang* and Miaoqi Li and Kerry Cawse-Nicholson and Amy J Braverman
Companies: University of Cincinnati and University of Cincinnati and Jet Propulsion Laboratory, California Institute of Technology and Jet Propulsion Laboratory, California Institute of Technology
Keywords: Basis functions; cross-covariance function; Gaussian Markov random fields; spatial prediction

We propose a multivariate fused Gaussian process (MFGP) model that is able to flexibly model multivariate spatial processes and enables efficient computation. Built up the Fused Gaussian process (FGP; Ma and Kang, 2019), the proposed model combines a low-rank component and a multivariate Gaussian Markov random field to jointly depict spatial dependence structure that is potentially nonstationary. Numerical studies are carried out to demonstrate the performance of the proposed method. The proposed method is also implemented in an uncertainty quantification simulation experiment to emulate multivariate geophysical processes.

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

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