Modeling rainfall data using a Bayesian Kriged-Kalman model

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.


[img] PDF kkfrainfall.pdf - Other
Download (711kB)


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.

Item Type: Book Section
ISBNs: 190574000 (print)
Related URLs:
Keywords: gibbs sampler, kalman filter, kriging, markov chain monte carlo, spatial temporal modeling, state-space model

Organisations: Statistics
ePrint ID: 30052
Date :
Date Event
Date Deposited: 10 May 2007
Last Modified: 16 Apr 2017 22:20
Further Information:Google Scholar

Actions (login required)

View Item View Item