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Statistical Analysis of Climate Series Analyzing, Plotting, Modeling, and Predicting with R /

The book presents the application of statistical methods to climatological data on temperature and precipitation. It provides specific techniques for treating series of yearly, monthly and daily records. The results' potential relevance in the climate context is discussed. The methodical tools...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Pruscha, Helmut (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Temas:
Acceso en línea:Texto Completo

MARC

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100 1 |a Pruscha, Helmut.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
245 1 0 |a Statistical Analysis of Climate Series  |h [electronic resource] :  |b Analyzing, Plotting, Modeling, and Predicting with R /  |c by Helmut Pruscha. 
250 |a 1st ed. 2013. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2013. 
300 |a VIII, 176 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a Climate series -- Trend and Season -- Correlation: From Yearly to Daily Data -- Model and Prediction: Yearly Data -- Model and Prediction: Monthly Data -- Analysis of Daily Data -- Spectral Analysis -- Complements -- Appendices: A: Excerpt from Climate Data Sets -- B: Some Aspects of Time Series -- C:Categorical Data Analysis- References -- Index. 
520 |a The book presents the application of statistical methods to climatological data on temperature and precipitation. It provides specific techniques for treating series of yearly, monthly and daily records. The results' potential relevance in the climate context is discussed. The methodical tools are taken from time series analysis, from periodogram and wavelet analysis, from correlation and principal component analysis, and from categorical data and event-time analysis. The applied models are - among others - the ARIMA and GARCH model, and inhomogeneous Poisson processes. Further, we deal with a number of special statistical topics, e.g. the problem of trend-, season- and autocorrelation-adjustment, and with simultaneous statistical inference. Programs in R and data sets on climate series, provided at the author's homepage, enable readers (statisticians, meteorologists, other natural scientists) to perform their own exercises and discover their own applications. 
650 0 |a Statistics . 
650 0 |a Climatology. 
650 0 |a Mathematical statistics-Data processing. 
650 0 |a Atmospheric science. 
650 1 4 |a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 
650 2 4 |a Climate Sciences. 
650 2 4 |a Statistical Theory and Methods. 
650 2 4 |a Statistics and Computing. 
650 2 4 |a Atmospheric Science. 
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776 0 8 |i Printed edition:  |z 9783642320859 
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776 0 8 |i Printed edition:  |z 9783642320835 
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950 |a Mathematics and Statistics (SpringerNature-11649) 
950 |a Mathematics and Statistics (R0) (SpringerNature-43713)