OCEANDATAMODELS

Statistical modelling and estimation for spatiotemporal data with oceanographic applications

Coordinatore	UNIVERSITY COLLEGE LONDON    more  Organization address address: GOWER STREET city: LONDON postcode: WC1E 6BT contact info Titolo: Mr. Nome: Giles Cognome: Machell Email: send email Telefono: +44 20 3108 3020 Fax: +44 20 7813 2849
Nazionalità Coordinatore	United Kingdom [UK]
Totale costo	294˙219 €
EC contributo	294˙219 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2013-IOF
Funding Scheme	MC-IOF
Anno di inizio	2014
Periodo (anno-mese-giorno)	2014-04-01   -   2017-03-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	UNIVERSITY COLLEGE LONDON  Organization address address: GOWER STREET city: LONDON postcode: WC1E 6BT contact info Titolo: Mr. Nome: Giles Cognome: Machell Email: send email Telefono: +44 20 3108 3020 Fax: +44 20 7813 2849	UK (LONDON)	coordinator	294˙219.60

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 Obiettivo del progetto (Objective)

'This fellowship is concerned with building new statistical modelling and estimation procedures that are appropriate for Big Data challenges with high-dimensional dependent data. The new methods will be applied to large oceanographic spatiotemporal datasets leading to important application benefits in global climate modelling. The methodological contribution centres on building physically-motivated stochastic processes that capture multivariate dependence structure from complex high-dimensional data sets. Estimation procedures are then developed to capture heterogeneity in spatiotemporal data, while properly accounting for practical issues such as irregularly-sampled data in space and time. Such modelling and estimation procedures provide great interpretability and meaningful summaries from the complex data sets we observe. The societal benefits include improved global climate modelling and improved responses to environmental disasters such as oil spills.

These advances will be achieved through interdisciplinary collaboration, with the fellow working closely with world-leading experts in oceanographic data in the US during the outgoing phase, and then consolidating these developments at the UCL Department of Statistical Sciences in the return phase. The fellow will therefore gain experience in developing relevant new statistical methods for a pressing Big Data challenge, and will then return to Europe where this training will significantly develop the fellow’s ability to produce cutting-edge research at the frontier of statistics and numerous applications involving complex high-dimensional data.'