| DC Field | Value | Language |
|---|
| dc.contributor.author | Cryer, Jonathan D | - |
| dc.date.accessioned | 2021-04-19T06:39:06Z | - |
| dc.date.available | 2021-04-19T06:39:06Z | - |
| dc.date.issued | 2008 | - |
| dc.identifier.isbn | : 978-0-387-75959-3 | - |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/69 | - |
| dc.description | This book is a second edition of the book Time Series Analysis by Jonathan Cryer,
published in 1986 by PWS-Kent Publishing (Duxbury Press). This new edition contains
nearly all of the well-received original in addition to considerable new material, numer-
ous new datasets, and new exercises. Some of the new topics that are integrated with the
original include unit root tests, extended autocorrelation functions, subset ARIMA mod-
els, and bootstrapping. Completely new chapters cover the topics of time series regres-
sion models, time series models of heteroscedasticity, spectral analysis, and threshold
models. Although the level of difficulty in these new chapters is somewhat higher than
in the more basic material, we believe that the discussion is presented in a way that will
make the material accessible and quite useful to a broad audience of users. Chapter 15,
Threshold Models, is placed last since it is the only chapter that deals with nonlinear
time series models. It could be covered earlier, say after Chapter 12. Also, Chapters 13
and 14 on spectral analysis could be covered after Chapter 10.
We would like to thank John Kimmel, Executive Editor, Statistics, at Springer, for
his continuing interest and guidance during the long preparation of the manuscript. Pro-
fessor Howell Tong of the London School of Economics, Professor Henghsiu Tsai of
Academica Sinica, Taipei, Professor Noelle Samia of Northwestern University, Profes-
sor W. K. Li and Professor Kai W. Ng, both of the University of Hong Kong, and Profes-
sor Nils Christian Stenseth of the University of Oslo kindly read parts of the manuscript,
and Professor Jun Yan used a preliminary version of the text for a class at the University
of Iowa. Their constructive comments are greatly appreciated. We would like to thank
Samuel Hao who helped with the exercise solutions and read the appendix: An Introduc-
tion to R. We would also like to thank several anonymous reviewers who read the manu-
script at various stages. Their reviews led to a much improved book. Finally, one of the
authors (JDC) would like to thank Dan, Marian, and Gene for providing such a great
place, Casa de Artes, Club Santiago, Mexico, for working on the first draft of much of
this new edition. | en_US |
| dc.description.abstract | The theory and practice of time series analysis have developed rapidly since the appear-
ance in 1970 of the seminal work of George E. P. Box and Gwilym M. Jenkins, Time
Series Analysis: Forecasting and Control, now available in its third edition (1994) with
co-author Gregory C. Reinsel. Many books on time series have appeared since then, but
some of them give too little practical application, while others give too little theoretical
background. This book attempts to present both application and theory at a level acces-
sible to a wide variety of students and practitioners. Our approach is to mix application
and theory throughout the book as they are naturally needed.
The book was developed for a one-semester course usually attended by students in
statistics, economics, business, engineering, and quantitative social sciences. Basic
applied statistics through multiple linear regression is assumed. Calculus is assumed
only to the extent of minimizing sums of squares, but a calculus-based introduction to
statistics is necessary for a thorough understanding of some of the theory. However,
required facts concerning expectation, variance, covariance, and correlation are
reviewed in appendices. Also, conditional expectation properties and minimum mean
square error prediction are developed in appendices. Actual time series data drawn from
various disciplines are used throughout the book to illustrate the methodology. The book
contains additional topics of a more advanced nature that can be selected for inclusion in
a course if the instructor so chooses | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Springer | en_US |
| dc.subject | Time Series | en_US |
| dc.subject | Statistics & Actuarial Science | en_US |
| dc.title | Time Series Analysis | en_US |
| dc.type | Book | en_US |
| Appears in Collections: | ARTS & SCIENCE
|
