Efficient Bayesian estimation of multivariate state space models

Authors:
Chris M. Strickland;Ian. W. Turner;Robert Denham;Kerrie L. Mengersen
Affiliations:
School of Mathematics, GPO Box 2434, Queensland University of Technology, Queensland, 4001, Australia;School of Mathematics, GPO Box 2434, Queensland University of Technology, Queensland, 4001, Australia;Remote Sensing Centre, Department of Environment and Resource Management, 80 Meiers Rd, Indooroopilly, Queensland, 4068, Australia;School of Mathematics, GPO Box 2434, Queensland University of Technology, Queensland, 4001, Australia
Venue:
Computational Statistics & Data Analysis
Year:
2009

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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.