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Time Series [Chapman & Hall/CRC Texts in Statistical Science]

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ISBN
9780367221096
Subject Area
Mathematics, Computers
Publication Name
Time Series : a Data Analysis Approach Using R
Publisher
CRC Press LLC
Item Length
9.4 in
Subject
Programming Languages / General, Probability & Statistics / General, Probability & Statistics / Time Series
Publication Year
2018
Series
Chapman and Hall/Crc Texts in Statistical Science Ser.
Type
Textbook
Format
Hardcover
Language
English
Item Height
0.9 in
Author
David Stoffer, Robert Shumway
Item Weight
15.3 Oz
Item Width
6.4 in
Number of Pages
272 Pages

關於產品

Product Identifiers

Publisher
CRC Press LLC
ISBN-10
0367221098
ISBN-13
9780367221096
eBay Product ID (ePID)
2309310264

Product Key Features

Number of Pages
272 Pages
Language
English
Publication Name
Time Series : a Data Analysis Approach Using R
Publication Year
2018
Subject
Programming Languages / General, Probability & Statistics / General, Probability & Statistics / Time Series
Type
Textbook
Subject Area
Mathematics, Computers
Author
David Stoffer, Robert Shumway
Series
Chapman and Hall/Crc Texts in Statistical Science Ser.
Format
Hardcover

Dimensions

Item Height
0.9 in
Item Weight
15.3 Oz
Item Length
9.4 in
Item Width
6.4 in

Additional Product Features

Intended Audience
College Audience
LCCN
2019-018441
Reviews
"The intended audience of the book are mathematics undergraduates taking a one semester course on time series. . . The authors frame learning time series primarily by extending concepts from linear models. Personally, I favour this approach, since it allows the book to clearly signpost similarities and differences between concepts in both topics and provides a natural learning progression from what most undergraduate students will already be familiar with . . .This book successfully delivers a practical tool-based approach to time series analysis at an introductory level, complementing the existing texts from the authors, which are aimed at a more advanced audience." ~Matthew Nunes, Journal Times Series Analysis
Illustrated
Yes
Table Of Content
1. Time Series Characteristics. 2. Time Series Regression and EDA. 3. ARIMA Models. 4. Spectral Analysis and Filtering. 5. Some Additional Topics.
Synopsis
This textbook is designed for an introductory time series course where the prerequisites are an understanding of linear regression and some basic probability skills. All of the numerical examples were done using the R statistical package, and the code is typically listed at the end of an example., The goals of this text are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data. A useful feature of the presentation is the inclusion of nontrivial data sets illustrating the richness of potential applications to problems in the biological, physical, and social sciences as well as medicine. The text presents a balanced and comprehensive treatment of both time and frequency domain methods with an emphasis on data analysis. Numerous examples using data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and the analysis of economic and financial problems. The text can be used for a one semester/quarter introductory time series course where the prerequisites are an understanding of linear regression, basic calculus-based probability skills, and math skills at the high school level. All of the numerical examples use the R statistical package without assuming that the reader has previously used the software. Robert H. Shumway is Professor Emeritus of Statistics, University of California, Davis. He is a Fellow of the American Statistical Association and has won the American Statistical Association Award for Outstanding Statistical Application. He is the author of numerous texts and served on editorial boards such as the Journal of Forecasting and the Journal of the American Statistical Association . David S. Stoffer is Professor of Statistics, University of Pittsburgh. He is a Fellow of the American Statistical Association and has won the American Statistical Association Award for Outstanding Statistical Application. He is currently on the editorial boards of the Journal of Forecasting , the Annals of Statistical Mathematics , and the Journal of Time Series Analysis . He served as a Program Director in the Division of Mathematical Sciences at the National Science Foundation and as an Associate Editor for the Journal of the American Statistical Association and the Journal of Business & Economic Statistics .
LC Classification Number
QA280.S5845 2019
ebay_catalog_id
4
Copyright Date
2019

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