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Statistical and computational methods for the analysis of spatial data

Objective



The proposal covers a number of areas of spatial statistics, including There will be seven postdoctoral researchers, most if not all recent PhDs. geostatistical and lattice models, random point processes and stochastic We expect them to visit several centres and pursue joint research with different partners. The benefit to the researchers involved will be geometry problems such as deformable template models and tessellations. participation in a very vigorous area of statistical research with much Major areas of application include imaging, and the use of spatial and opportunity for subsequent employment in academia and in industry. spatial-temporal models in analysing environmental data. WE also consider models ?or spatial data based on mixtures. A common theme is the need for simulation-based methods of data analysis, in particular Markov chain Monte Carlo (MCMC) but also other techniques such as bootstrapping. This in turn leads us into theoretical analysis of MCMC and bootstrapping algorithms. A major theme of the proposed research is the development of original theoretical results for MCMC and bootstrap in the context of spatial models. A final theme is the study of extremal properties of spatial processes, motivated by a number of environmental applications.

Coordinator

THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
Address
Wilberforce Road
CB3 0WB Cambridge
United Kingdom

Participants (6)

Athens University of Economics and Business
Greece
Address
76,Patission Street 76
10434 Athens
CHALMERS UNIVERSITY OF TECHNOLOGY
Sweden
Address
2,Sven Hultins Gata 2
412 96 Goeteborg
Centre National de la Recherche Scientifique
France
Address

76821 Mont-saint-aignan
UNIVERSITA DEGLI STUDI DI ROMA TRE
Italy
Address
Largo San Leonardo Murialdo, 1
00146 Roma
University of Lancaster
United Kingdom
Address
Bailrigg
LA1 4YW Lancaster
Ålborg Universitet
Denmark
Address
7E,fredrik Bajers Vej 7E
9220 Ëlborg