Analyzing, Plotting, Modeling, and Predicting with R
2013, VIII, 176 p.
Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.
You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.
After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.
Within the context of the general climate discussion, the evaluation of climate series gains growing importance
Provides application of statistical methods to climatological data Techniques for treating series records
Applying among others ARIMA and GARCH model Programs in R and data sets on climate series are provided at the author's homepage
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.
Content Level »Research
Keywords »Climate Series - Computing in R - Statistical Analysis