Fast Bayesian analysis of spatial dynamic factor models

Strickland, Christopher M., Simpson, Daniel P., Turner, Ian W., Denham, Robert, & Mengersen, Kerrie L. (2008) Fast Bayesian analysis of spatial dynamic factor models. [Working Paper] (Submitted (not yet accepted for publication))


A Bayesian Markov chain Monte Carlo (MCMC) 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. The data set focuses on a region in central Queensland, Australia, which contains two landtype classes. The spatial DFM is used to extract both the landtype information and the associated common factors in the analysis.

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184 since deposited on 17 Dec 2008
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ID Code: 16991
Item Type: Working Paper
Refereed: No
Keywords: Bayesian Analysis, Spatial dynamic factor model, Gaussian Markov random field, Krylov subspace method, MODIS, Markov chain Monte Carlo
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Statistics not elsewhere classified (010499)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2008 [please consult the authors]
Deposited On: 17 Dec 2008 02:13
Last Modified: 10 Aug 2011 13:50

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