A Bayesian approach for the estimation of the covariance structure of separable spatio-temporal stochastic processes
S. Bozza, and A. O'Hagan. Between Data Science and Applied Data Analysis: Proceedings of the 26th Annual Conference of the Gesellschaft Fűr Klassifikation Ev, 26, page 165. University of Mannheim, Springer Verlag, (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.
%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
}