University of Sheffield

Tony O'Hagan - Academic pages - Abstracts


A Bayesian Approach for the Estimation of the Covariance Structure of Separable Spatio-temporal Stochastic Processes

Silvia Bozza and Anthony O'Hagan

Dipartimento di Statistica, Universita di Venezia, Venezia, Italy and Department of Probability and Statistics, University of Sheffield, Sheffield, England

Publication details: In Between Data Science and Applied Data Analysis, M. Schader, W. Gaul and M. Vichi (eds), 165-172. Springer-Verlag. 2003.


In this paper, we address the problem of estimating the dependence structure of spatio-temporal stochastic processes. Starting from the assumption of separability, we propose a Bayesian semiparametric model that allows non-stationary spatial-temporal dependence structures. The model provides a conjoint estimation of the spatial and temporal covariance structures, with a hierarchical model internally to model the temporal dependence. A simulated case study is reported.

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Updated: 25 August 2004
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