Modeling rainfall data using a Bayesian Kriged-Kalman model
Modeling rainfall data using a Bayesian Kriged-Kalman model
A suitable model for analyzing rainfall data needs to take into account variation in both space and time. The method of kriging is a popular approach in spatial statistics which makes predictions for spatial data. Kalman filtering using dynamic models is often used to analyze temporal data. These approaches have been combined in a classical framework termed kriged Kalman filter (KKF) model. In the combined model, the kriging predictions dictate the optimal regression surface for incorporating spatial structure and the dynamic linear model framework is used to learn about temporal factors such as trends, autoregressive components and cyclical variations. In this article we consider a full Bayesian KKF (BKKF) model for rainfall data and its MCMC implementation. The MCMC techniques provide unified estimation of spatio-temporal effects and allow optimal predictions in time and space. The methods are illustrated with two real data examples. Using many well known validation methods we highlight the advantages of the BKKF model.
gibbs sampler, kalman filter, kriging, markov chain monte carlo, spatial temporal modeling, state-space model
190574000X
61-86
Jona Lasinio, Giovanna
7b18fe69-161f-4ce8-b0b5-9f491afa357c
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Mardia, Kant V.
71cada2a-0900-4c30-940d-29e496b5ccf4
2007
Jona Lasinio, Giovanna
7b18fe69-161f-4ce8-b0b5-9f491afa357c
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Mardia, Kant V.
71cada2a-0900-4c30-940d-29e496b5ccf4
Jona Lasinio, Giovanna, Sahu, Sujit K. and Mardia, Kant V.
(2007)
Modeling rainfall data using a Bayesian Kriged-Kalman model.
In,
Upadhy, S.K., Singh, Umesh and Dey, Dipak K.
(eds.)
Bayesian Statistics and its Applications.
Tunbridge Wells, UK.
Anshan, .
Record type:
Book Section
Abstract
A suitable model for analyzing rainfall data needs to take into account variation in both space and time. The method of kriging is a popular approach in spatial statistics which makes predictions for spatial data. Kalman filtering using dynamic models is often used to analyze temporal data. These approaches have been combined in a classical framework termed kriged Kalman filter (KKF) model. In the combined model, the kriging predictions dictate the optimal regression surface for incorporating spatial structure and the dynamic linear model framework is used to learn about temporal factors such as trends, autoregressive components and cyclical variations. In this article we consider a full Bayesian KKF (BKKF) model for rainfall data and its MCMC implementation. The MCMC techniques provide unified estimation of spatio-temporal effects and allow optimal predictions in time and space. The methods are illustrated with two real data examples. Using many well known validation methods we highlight the advantages of the BKKF model.
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kkfrainfall.pdf
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Published date: 2007
Keywords:
gibbs sampler, kalman filter, kriging, markov chain monte carlo, spatial temporal modeling, state-space model
Organisations:
Statistics
Identifiers
Local EPrints ID: 30052
URI: http://eprints.soton.ac.uk/id/eprint/30052
ISBN: 190574000X
PURE UUID: 8ad2bf6f-7313-4598-ba7e-160a1b918c43
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Date deposited: 10 May 2007
Last modified: 16 Mar 2024 03:15
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Contributors
Author:
Giovanna Jona Lasinio
Author:
Kant V. Mardia
Editor:
S.K. Upadhy
Editor:
Umesh Singh
Editor:
Dipak K. Dey
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