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010   $a3-030-01584-X
024 7 $a10.1007/978-3-030-01584-8
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035   $a(MiAaPQ)EBC5627320
035   $a(DE-He213)978-3-030-01584-8
035   $a(PPN)232966796
035   $a(EXLCZ)994100000007335145
100   $a20181230d2018      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aQuantitative Methods in Environmental and Climate Research /$fedited by Michela Cameletti, Francesco Finazzi
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (136 pages)
311 08$a3-030-01583-1 
327   $a1 Fast Bayesian classification for disease mapping and the detection of disease clusters -- 2 A Novel Hierarchical Multinomial Approach to Modelling Age-specific Harvest Data -- 3 Detection of change points in spatiotemporal data in presence of outliers and heavy-tailed observations -- 4 Modelling spatiotemporal mismatch for Aerosol profiles -- 5 A SPATIOTEMPORAL APPROACH FOR PREDICTING WIND SPEED ALONG THE COAST OF VALPARAISO, CHILE -- 6 Spatiotemporal Precipitation Variability Modeling in the Blue Nile Basin: 1998-2016 -- 7 A hidden Markov random field with copula-based emission distributions for the analysis of spatial cylindrical data.
330   $aThis 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. .
606   $aStatistics
606   $aEcology
606   $aEcology
606   $aStatistics
606   $aClimatology
606   $aEnvironmental monitoring
606   $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
606   $aTheoretical and Statistical Ecology
606   $aEnvironmental Sciences
606   $aStatistical Theory and Methods
606   $aClimate Sciences
606   $aEnvironmental Monitoring
615  0$aStatistics.
615  0$aEcology.
615  0$aEcology.
615  0$aStatistics.
615  0$aClimatology.
615  0$aEnvironmental monitoring.
615 14$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
615 24$aTheoretical and Statistical Ecology.
615 24$aEnvironmental Sciences.
615 24$aStatistical Theory and Methods.
615 24$aClimate Sciences.
615 24$aEnvironmental Monitoring.
676   $a551.6072
676   $a551.6
702   $aCameletti$b Michela$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aFinazzi$b Francesco$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910303445803321
996   $aQuantitative Methods in Environmental and Climate Research$91563728
997   $aUNINA