Padilla, LeonardoLeonardoPadillaLagos-Alvarez, BernadoBernadoLagos-AlvarezMateu, JorgeJorgeMateuPorcu, EmilioEmilioPorcu2026-10-192026-10-192020-11Padilla, Leonardo; Lagos-Alvarez, Bernado; Mateu, Jorge; Porcu, Emilio (2020-11). Space-time autoregressive estimation and prediction with missing data based on Kalman filtering. ENVIRONMETRICS, 31 (7), e2627. https://doi.org/10.1002/env.26271180-40091099-095Xhttps://hdl.handle.net/20.500.12740/25371We 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.enautoregressive space-time modelgeneral expectation-maximization algorithmmaximum likelihoodmissing datastate-space systemArticulohttps://doi.org/10.1002/env.2627