%0 Conference Paper %1 bozza_bayesian_2003 %A Bozza, Silvia %A O'Hagan, Anthony %B Between Data Science and Applied Data Analysis: Proceedings of the 26th Annual Conference of the Gesellschaft Fűr Klassifikation Ev %C University of Mannheim %D 2003 %E Schader, M. %E Gaul, W. %E Vichi, M. %I Springer Verlag %K Bayesian \_tablet\_modified, autocorrelation autocorrelation, covariance hierarchical inference, matrix, model, semiparametric, spatial temporal temporal, %P 165 %T A Bayesian approach for the estimation of the covariance structure of separable spatio-temporal stochastic processes %U ftp://ftp.uni-bayreuth.de/pub/math/statlib/DOS/general/first-bayes/pdf/paperkorr.pdf %V 26 %X 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 nonstationary spatial-temporal dependence structures. The model provides an estimation of the spatial and temporal covariance structures, with a hierarchical model internally to model the temporal dependence. A simulated case study is reported.

@inproceedings{bozza_bayesian_2003, abstract = {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 nonstationary spatial-temporal dependence structures. The model provides an estimation of the spatial and temporal covariance structures, with a hierarchical model internally to model the temporal dependence. A simulated case study is reported.}, added-at = {2017-01-09T13:57:26.000+0100}, address = {University of Mannheim}, author = {Bozza, Silvia and O'Hagan, Anthony}, biburl = {https://www.bibsonomy.org/bibtex/23b8235be9c191b7d3bfff1a46ab29f5b/yourwelcome}, booktitle = {Between {Data} {Science} and {Applied} {Data} {Analysis}: {Proceedings} of the 26th {Annual} {Conference} of the {Gesellschaft} {Fűr} {Klassifikation} {Ev}}, editor = {Schader, M. and Gaul, W. and Vichi, M.}, interhash = {ee58969e0334624962dcd8e67f382e8d}, intrahash = {3b8235be9c191b7d3bfff1a46ab29f5b}, keywords = {Bayesian \_tablet\_modified, autocorrelation autocorrelation, covariance hierarchical inference, matrix, model, semiparametric, spatial temporal temporal,}, pages = 165, publisher = {Springer Verlag}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {A {Bayesian} approach for the estimation of the covariance structure of separable spatio-temporal stochastic processes}, url = {ftp://ftp.uni-bayreuth.de/pub/math/statlib/DOS/general/first-bayes/pdf/paperkorr.pdf}, urldate = {2012-08-09}, volume = 26, year = 2003 }