Details of Grant 

EPSRC Reference:	GR/L56206/01
Title:	MODEL-BASED GEOSTATISTICS
Principal Investigator:	Diggle, Professor PJ
Other Investigators:	Tawn, Professor J Coles, Dr S
Researcher Co-Investigators:
Project Partners:
Department:	Mathematics and Statistics
Organisation:	Lancaster University
Scheme:	Standard Research (Pre-FEC)
Starts:	01 January 1998	Ends:	30 June 2001	Value (£):	103,236
EPSRC Research Topic Classifications:	Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:	No relevance to Underpinning Sectors
Related Grants:
Panel History:
Summary on Grant Application Form
The proposal aims to develop a model-based statistical framework for analysing spatial data. The approach will provide far greater generality and flexibility than the existing geostatistical methods based on kriging. The novel approach to be taken is to extend the modelling procedures of Diggle, Moyeed and Tawn (1996) that model spatial data as observations from a generalised linear model with mean effect given by a latent Gaussian process. These procedures allow the structure of the data to be exploited and non-linear functionals of the process predicted. Markov chain Monte Carlo procedures currently used for inference and prediction in this case are limited by the number of different data locations and by the dimension of the variable observed at each site. We aim to combine Markov random field methods with the model of a Gaussian latent process to overcome such difficulties. Diagnostic tools and techniques for handling censored and multivariate data will be developed. Within this framework features of spatial design and inference will be uniquely assessed allowing for parameter uncertainty. A key application are is to spatial extreme value data for which existing methods are essentially low dimensional multivariate models. More generally the new methodology will be applied and tested using large-scale environmental systems with complex data structures.
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Organisation Website:	http://www.lancs.ac.uk