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Activity Number: 172 - Machine Learning and Algorithms
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #323227 View Presentation
Title: Matrix-Free Computation of Spatial-Temporal Gaussian Autoregressions and Related Stat-Space Models
Author(s): Chunxiao Wang* and Debashis Mondal
Companies: Oregon State University and Oregon State University
Keywords: H-likelihood ; Kalman filter ; Lanczos algorithm ; Matrix-free computation ; State space model
Abstract:

We combines Besag's spatial-temporal Gaussian autoregressions within a state-space model and presents a novel matrix-free h-likelihood method for statistical inference. The inference proposed here is shown to have significant computational advantages compared with traditional Kalman filter method. It includes a novel matrix-free scalable Lanczos algorithm that the best linear unbiased predictors by solving Henderson's mixed equations and a novel matrix-free trust region method that finds the solution of the REML score equations. Furthermore, we pays careful attention to computations in small time steps and indicates how the h-likelihood method can be adapted for statistical inference of other space-time models, such as stochastic injection-diffusions, blurred generated models and other spatial-temporal processes based on stochastic partial differential equations. The method is illustrated through a simulation study and soil moisture data application.


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

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