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Modeling rainfall data using a Bayesian Kriged-Kalman model

Record type: Book Section

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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Citation

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 pp. 61-86.

More information

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
ORCID for Sujit K. Sahu: ORCID iD orcid.org/0000-0003-2315-3598

Catalogue record

Date deposited: 10 May 2007
Last modified: 17 Jul 2017 15:55

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Contributors

Author: Giovanna Jona Lasinio
Author: Sujit K. Sahu ORCID iD
Author: Kant V. Mardia
Editor: S.K. Upadhy
Editor: Umesh Singh
Editor: Dipak K. Dey

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