Space‐time autoregressive estimation and prediction with missing data based on Kalman filtering-Reference-Cited by-同舟云学术

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

Published:2020-05-11 Issue:7 Volume:31 Page:
ISSN:1180-4009
Container-title:Environmetrics
language:en
Short-container-title:Environmetrics

Author:

Padilla Leonardo¹,Lagos‐Álvarez Bernado¹,Mateu Jorge²,Porcu Emilio³⁴

Affiliation:

1. Department of Statistics University of Concepción Concepción Chile

2. Department of Mathematics University Jaume I Castellón Spain

3. School of Mathematics, Statistics and Physics Newcastle University Newcastle upon Tyne UK

4. Department of Mathematics University of Atacama Copiapó Chile

Publisher

Wiley

Subject

Ecological Modelling,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/env.2627

Reference30 articles.

1. Using a spatio-temporal dynamic state-space model with the EM algorithm to patch gaps in daily riverflow series

2. Anderson B. D.andMoore J. B.(1979).Optimal filtering.Englewood Cliffs NJ:Courier Corporation 21:22–95.

3. Spatial and Spatio-temporal Bayesian Models with R-INLA

4. Maximum likelihood estimation of the multivariate hidden dynamic geostatistical model with application to air quality in Apulia, Italy

5. Spatio-temporal modeling of particulate matter concentration through the SPDE approach

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