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Activity Number: 150 - Methods and Computing for Spatial and Spatio-Temporal Data
Type: Contributed
Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #322804
Title: Constructing Large Nonstationary Spatio-Temporal Covariance Models via Compositional Warpings
Author(s): Quan Vu* and Andrew Zammit Mangion and Stephen Chuter
Companies: University of Wollongong and University of Wollongong and University of Bristol
Keywords: Deformation; Gaussian Process; Nonseparable; Vecchia Approximation; Environmental Statistics; Spatial Statistics
Abstract:

Understanding and predicting environmental phenomena often requires the construction of spatio-temporal statistical models, which are typically Gaussian processes. A common assumption made on Gausian processes is that of covariance stationarity, which is unrealistic in many geophysical applications. In this talk, we introduce a new approach to construct descriptive nonstationary spatio-temporal models by modeling stationary processes on warped spatio-temporal domains. The warping functions we use are constructed using several simple injective warping units which, when combined through composition, can induce complex warpings. A stationary spatio-temporal covariance function on the warped domain induces covariance nonstationarity on the original domain. Sparse linear algebraic methods are used to reduce the computational complexity when fitting the model in a big data setting. We show that our proposed nonstationary spatio-temporal model can capture covariance nonstationarity in both space and time, and provide better probabilistic predictions than conventional stationary models in both simulation studies and on a real-world data set.


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

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