Fast Bayesian analysis of spatial dynamic factor models for large space time data sets

Strickland, Christopher Mark, Simpson, Daniel P., Turner, Ian W., Denham, Robert, & Mengersen, Kerrie L. (2009) Fast Bayesian analysis of spatial dynamic factor models for large space time data sets. [Working Paper] (Submitted (not yet accepted for publication))


Remoting sensing is one example where data sets that vary across space and time have become so large that `standard' approaches employed by statistical modellers for applied analysis are no longer feasible. In this paper, we present a Bayesian methodology, which makes use of recently developed algorithms in applied mathematics, for the analysis of large space time data sets. In particular, a Markov chain Monte Carlo algorithm is proposed for the efficient estimation of spatial dynamic factor models (DFMs). The spatial DFM is specified whereby spatial dependence is modelled though the columns of the factor loadings matrix using a Gaussian Markov random field. Krylov subspace methods are used to take advantage of the sparse matrix structures that are inherent in the model. The methodology is used to analyse remotely sensed data from the Moderate Imaging Spectroradiometer satellite. In particular, the proposed methodology is used in conjunction with high resolution imagery for the classification, in terms of land type, of two regions located in central Queensland, Australia.

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ID Code: 20179
Item Type: Working Paper
Refereed: No
Additional Information: This paper is an updated version of the paper titled, "Fast Bayesian Analysis of Spatial Dynamic Factor Models." This version is to be linked to the version that is currently in the Eprints archive.
Keywords: Bayesian analysis, Markov chain Monte Carlo, MODIS, Gaussian Markov random fields, Krylov subspace method, Spatial dynamic factor model
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2009 [please consult the authors]
Deposited On: 28 Apr 2009 05:29
Last Modified: 22 Jun 2017 14:41

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