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Space-time autoregressive estimation and prediction with missing data based on Kalman filtering

Lookup NU author(s): Professor Emilio Porcu

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Abstract

© 2020 John Wiley & Sons, Ltd. We propose a Kalman filter algorithm to provide a formal statistical analysis of space-time data with an autoregressive structure in time. The Kalman filter technique allows to capture the temporal dependence as well as the spatial correlation structure through state-space equations, and it is aimed to perform statistical inference in terms of parameter estimation and prediction at unobserved locations. We thus develop space-time estimation and prediction methods in the presence of missing data, through the Kalman filter, in order to obtain accurate estimates of model parameters and reliable space-time predictions. Our findings are illustrated through an application on daily air temperatures in some regions of southern Chile, where the dataset shows a number of missing data in many locations.

Publication metadata

Author(s): Padilla L, Lagos-Alvarez B, Mateu J, Porcu E

Publication type: Article

Publication status: Published

Journal: Environmetrics

Year: 2020

Volume: 31

Issue: 7

Print publication date: 01/11/2020

Online publication date: 11/05/2020

Acceptance date: 08/02/2020

ISSN (print): 1180-4009

ISSN (electronic): 1099-095X

Publisher: John Wiley and Sons Ltd

URL: https://doi.org/10.1002/env.2627

DOI: 10.1002/env.2627

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