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130626 ||| eng |
| 020 |
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|a 9783642320842
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| 100 |
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|a Pruscha, Helmut
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| 245 |
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|a Statistical Analysis of Climate Series
|h Elektronische Ressource
|b Analyzing, Plotting, Modeling, and Predicting with R
|c by Helmut Pruscha
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| 250 |
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|a 1st ed. 2013
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2013, 2013
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| 300 |
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|a VIII, 176 p
|b online resource
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| 505 |
0 |
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|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
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| 653 |
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|a Statistical Theory and Methods
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| 653 |
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|a Climatology
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| 653 |
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|a Statistics
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| 653 |
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|a Climate Sciences
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| 653 |
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|a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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| 653 |
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|a Atmospheric Science
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| 653 |
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|a Atmospheric science
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| 653 |
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|a Mathematical statistics / Data processing
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| 653 |
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|a Statistics and Computing
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
5 |
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|a 10.1007/978-3-642-32084-2
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| 856 |
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|u https://doi.org/10.1007/978-3-642-32084-2?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 519
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| 520 |
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|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
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