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

Leonardo Padilla, Bernado Lagos-Álvarez, Jorge Mateu, Emilio Porcu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageBritish English
Article numbere2627
JournalEnvironmetrics
Volume31
Issue number7
DOIs
StatePublished - 1 Nov 2020

Keywords

  • autoregressive space-time model
  • general expectation-maximization algorithm
  • maximum likelihood
  • missing data
  • state-space system

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