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TIES-GRASPA 2017 Conference on Climate and Environment

This books presents some of the most recent and advanced statistical methods used to analyse environmental and climate data, and addresses the spatial and spatio-temporal dimensions of the phenomena studied, the multivariate complexity of the data, and the necessity of considering uncertainty source...

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Detalles Bibliográficos
Autores principales: Cameletti, Michela, Finazzi, Francesco
Lenguaje:eng
Publicado: Springer 2018
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-030-01584-8
http://cds.cern.ch/record/2653137
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author Cameletti, Michela
Finazzi, Francesco
author_facet Cameletti, Michela
Finazzi, Francesco
author_sort Cameletti, Michela
collection CERN
description This books presents some of the most recent and advanced statistical methods used to analyse environmental and climate data, and addresses the spatial and spatio-temporal dimensions of the phenomena studied, the multivariate complexity of the data, and the necessity of considering uncertainty sources and propagation. The topics covered include: detecting disease clusters, analysing harvest data, change point detection in ground-level ozone concentration, modelling atmospheric aerosol profiles, predicting wind speed, precipitation prediction and analysing spatial cylindrical data. The volume presents revised versions of selected contributions submitted at the joint TIES-GRASPA 2017 Conference on Climate and Environment, which was held at the University of Bergamo, Italy. As it is chiefly intended for researchers working at the forefront of statistical research in environmental applications, readers should be familiar with the basic methods for analysing spatial and spatio-temporal data. .
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spelling cern-26531372021-04-22T06:33:21Zdoi:10.1007/978-3-030-01584-8http://cds.cern.ch/record/2653137engCameletti, MichelaFinazzi, FrancescoTIES-GRASPA 2017 Conference on Climate and EnvironmentMathematical Physics and MathematicsThis books presents some of the most recent and advanced statistical methods used to analyse environmental and climate data, and addresses the spatial and spatio-temporal dimensions of the phenomena studied, the multivariate complexity of the data, and the necessity of considering uncertainty sources and propagation. The topics covered include: detecting disease clusters, analysing harvest data, change point detection in ground-level ozone concentration, modelling atmospheric aerosol profiles, predicting wind speed, precipitation prediction and analysing spatial cylindrical data. The volume presents revised versions of selected contributions submitted at the joint TIES-GRASPA 2017 Conference on Climate and Environment, which was held at the University of Bergamo, Italy. As it is chiefly intended for researchers working at the forefront of statistical research in environmental applications, readers should be familiar with the basic methods for analysing spatial and spatio-temporal data. .Springeroai:cds.cern.ch:26531372018
spellingShingle Mathematical Physics and Mathematics
Cameletti, Michela
Finazzi, Francesco
TIES-GRASPA 2017 Conference on Climate and Environment
title TIES-GRASPA 2017 Conference on Climate and Environment
title_full TIES-GRASPA 2017 Conference on Climate and Environment
title_fullStr TIES-GRASPA 2017 Conference on Climate and Environment
title_full_unstemmed TIES-GRASPA 2017 Conference on Climate and Environment
title_short TIES-GRASPA 2017 Conference on Climate and Environment
title_sort ties-graspa 2017 conference on climate and environment
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-030-01584-8
http://cds.cern.ch/record/2653137
work_keys_str_mv AT camelettimichela tiesgraspa2017conferenceonclimateandenvironment
AT finazzifrancesco tiesgraspa2017conferenceonclimateandenvironment