Efficient Bayesian estimation of multivariate state space models |
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Authors: | Chris M. Strickland Ian. W. Turner Robert Denham Kerrie L. Mengersen |
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Affiliation: | aSchool of Mathematics, GPO Box 2434, Queensland University of Technology, Queensland, 4001, Australia;bRemote Sensing Centre, Department of Environment and Resource Management, 80 Meiers Rd, Indooroopilly, Queensland, 4068, Australia |
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Abstract: | 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 time series 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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