Efficient Bayesian Estimation of Multivariate State Space Models
Strickland, Christopher M., Turner, Ian W., Denham, Robert, & Mengersen, Kerrie L. (2008) Efficient Bayesian Estimation of Multivariate State Space Models. Computational Statistics & Data Analysis, 53(12), pp. 4116-4125.
A Bayesian Markov chain Monte Carlo methodology is developed for the estimation of multivariate linear Gaussian state space models. In particular, an efficient simulation smoothing algorithm is proposed that makes use of the univariate representation of the state space model. Substantial gains over existing algorithms in computational efficiency are achieved using the new simulation smoother for the analysis of high dimensional multivariate time series.
The methodology is used to analyse a multivariate timeseries dataset of the Normalised Difference Vegetation Index (NDVI), which is a proxy for the level of live vegetation, for a particular grazing property located in Queensland, Australia.
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|Item Type:||Journal Article|
|Additional Information:||This research was funded by an ARC linkage grant between QUT and the Department of National Resources and Water|
|Keywords:||Multivariate, State space model, Markov chain Monte Carlo, Kalman filter, Simulation smoother, Univiarate representation, MODIS, Stochastic cycle|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > NUMERICAL AND COMPUTATIONAL MATHEMATICS (010300) > Numerical Analysis (010301)|
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|
|Copyright Owner:||Copyright 2008 Elsevier|
|Deposited On:||15 Feb 2008|
|Last Modified:||04 Sep 2012 15:11|
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