Time Series Analysis: With Applications in R – Jonathan Cryer & Kung-Sik Chan
Time Series Analysis
With Applications in R
By: Jonathan Cryer & Kung-Sik Chan
Publisher: Springer (2008)
Pages: 491
Format: Ebook (PDF)
Description
Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticity, and threshold models. All of the ideas and methods are illustrated with both real and simulated data sets.
A unique feature of this edition is its integration with the R computing environment. The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text. Script files of R commands for each chapter are available for download. There is also an extensive appendix in the book that leads the reader through the use of R commands and the new R package to carry out the analyses.
About the authors
Jonathan Cryer is Professor Emeritus, University of Iowa, in the Department of Statistics and Actuarial Science. He is a Fellow of the American Statistical Association and received a Collegiate Teaching Award from the University of Iowa College of Liberal Arts and Sciences. He is the author of Statistics for Business: Data Analysis and Modeling, Second Edition, (with Robert B. Miller), the Minitab Handbook, Fifth Edition, (with Barbara Ryan and Brian Joiner), the Electronic Companion to Statistics (with George Cobb), Electronic Companion to Business Statistics (with George Cobb) and numerous research papers.
Kung-Sik Chan is Professor, University of Iowa, in the Department of Statistics and Actuarial Science. He is a Fellow of the American Statistical Association and the Institute of the Mathematical Statistics, and an Elected Member of the International Statistical Institute. He received a Faculty Scholar Award from the University of Iowa in 1996. He is the author of Chaos: A Statistical Perspective (with Howell Tong) and numerous research papers.
Table of contents (15 chapters)
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Introduction
Pages 1-10
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Fundamental Concepts
Pages 11-26
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Trends
Pages 27-54
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Models For Stationary Time Series
Pages 55-85
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Models For Nonstationary Time Series
Pages 87-107
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Model Specification
Pages 109-147
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Parameter Estimation
Pages 149-174
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Model Diagnostics
Pages 175-190
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Forecasting
Pages 191-226
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Seasonal Models
Pages 227-248
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Time Series Regression Models
Pages 249-276
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Time Series Models Of Heteroscedasticity
Pages 277-318
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Introduction To Spectral Analysis
Pages 319-350
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Estimating The Spectrum
Pages 351-382
-
Threshold Models
Pages 383-422
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