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Statistical Analysis of Climate Series: Analyzing, Plotting, Modeling, and Predicting with R, by Helmut Pruscha
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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.
- Sales Rank: #3572492 in Books
- Brand: Brand: Springer
- Published on: 2012-10-30
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .50" w x 6.14" l, .95 pounds
- Binding: Hardcover
- 176 pages
- Used Book in Good Condition
From the Back Cover
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.
About the Author
Helmut Pruscha, Professor for Mathematics, has served as Academic Director at the University of Munich’s Institute of Mathematics. Before doing so, he had worked for many years as a statistician at a Max-Planck-Institute for neurobiology. His research interests include topics concerning applied statistics and mathematical statistics, especially categorical time series and point processes. He has published several textbooks in German.
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