Shumway / Stoffer Time Series Analysis and Its Applications
2. Auflage 2006
ISBN: 978-0-387-36276-2
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark
With R Examples
E-Book, Englisch, 588 Seiten, Web PDF
Reihe: Springer Texts in Statistics
ISBN: 978-0-387-36276-2
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark
The second edition marks a substantial change to the ?rst edition. P- haps the most signi?cant change is the introduction of examples based on the freeware R package. The package, which runs on most operating systems, can be downloaded from The Comprehensive R Archive Network (CRAN) at http://cran. r-project. org/ or any one of its mirrors. Readers who have experience with the S-PLUS R package will have no problem working with R. For novices, R installs some help manuals, and CRAN supplies links to contributed tutorials such as R for Beginners. In our examples, we assume the reader has downloaded and installed R and has downloaded the nec- sary data ?les. The data ?les can be downloaded from the website for the text,http://www. stat. pitt. edu/stoffer/tsa2/ or any one of its mirrors. We will also provide additional code and other information of interest on the text’s website. Most of the material that would be given in an introductory course on time series analysis has associated R code. Although examples are given in R, the material is not R-dependent. In courses we have given using a preliminary version of the new edition of the text, students were allowed to use any package of preference. Although most students used R (or S-PLUS), a number of them completed the course successfully using other programs such R R R as ASTSA, MATLAB ,SAS , and SPSS . Another substantial change from the ?rst edition is that the material has beendividedintosmallerchapters.
Zielgruppe
Graduate
Autoren/Hrsg.
Weitere Infos & Material
Characteristics of Time Series.- Time Series Regression and Exploratory Data Analysis.- ARIMA Models.- Spectral Analysis and Filtering.- Additional Time Domain Topics.- State-Space Models.- Statistical Methods in the Frequency Domain.
