Spatio-temporal analysis with short- and long-memory dependence: a state-space approach
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https://doi.org/10.1007/s11749-017-0541-7 |
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Title
Spatio-temporal analysis with short- and long-memory dependence: a state-space approachDate
2018Publisher
Springer Berlin HeidelbergISSN
1133-0686; 1863-8260Bibliographic citation
FERREIRA, Guillermo; MATEU, Jorge; PORCU, Emilio. Spatio-temporal analysis with short-and long-memory dependence: a state-space approach. TEST, 2018, vol. 27, núm. 1, p. 221-245Type
info:eu-repo/semantics/articlePublisher version
https://link.springer.com/article/10.1007/s11749-017-0541-7Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
This paper deals with the estimation and prediction problems of spatio-temporal processes by using state-space methodology. The spatio-temporal process is represented through an infinite moving average decomposition. ... [+]
This paper deals with the estimation and prediction problems of spatio-temporal processes by using state-space methodology. The spatio-temporal process is represented through an infinite moving average decomposition. This expansion is well known in time series analysis and can be extended straightforwardly in space–time. Such an approach allows easy implementation of the Kalman filter procedure for estimation and prediction of linear time processes exhibiting both short- and long-range dependence and a spatial dependence structure given on the locations. Furthermore, we consider a truncated state-space equation, which allows to calculate an approximate likelihood for large data sets. The performance of the proposed Kalman filter approach is evaluated by means of several Monte Carlo experiments implemented under different scenarios, and it is illustrated with two applications. [-]
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TEST, 2018, vol. 27, núm. 1, p. 221-245Investigation project
The first author would like to express his thanks for the support from DIUC 215.014.024-1.0, established by the Universidad de Concepción and Postdoctoral scholarship from Conicyt, Chile, 2014 (Folio 74150023). Jorge Mateu’s research was supported by Grant MTM2013-43917-P from the Spanish Ministry of Science and Education, and Grant P1-1B2015-40 and Emilio Porcu’s research was supported by Fondecyt Regular Project from Ministery of Science and Education, Chile.Rights
© Sociedad de Estadística e Investigación Operativa 2017
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