Hilbertian spatial periodically correlated first order autoregressive models

H. Haghbin, Z. Shishebor () and A. Soltani

Advances in Data Analysis and Classification, 2014, vol. 8, issue 3, 303-319

Abstract: In this article, we consider Hilbertian spatial periodically correlated autoregressive models. Such a spatial model assumes periodicity in its autocorrelation function. Plausibly, it explains spatial functional data resulted from phenomena with periodic structures, as geological, atmospheric, meteorological and oceanographic data. Our studies on these models include model building, existence, time domain moving average representation, least square parameter estimation and prediction based on the autoregressive structured past data. We also fit a model of this type to a real data of invisible infrared satellite images. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Hilbert spaces; Spatial autoregressive random fields; Periodically correlated processes; Functional data; 62M30; 62M40 (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s11634-014-0172-8

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