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Time Series Analysis (Springer Texts in Statistics)
 
 

Time Series Analysis (Springer Texts in Statistics) [Kindle Edition]

Jonathan D. Cryer , Kung-Sik Chan
4.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)

Kindle-Preis: EUR 53,26 Inkl. MwSt. und kostenloser drahtloser Lieferung über Amazon Whispernet

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Produktbeschreibungen

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From the reviews of the second edition.

"The book is ideal for undergradute and honours time series modules, . . . .written and structured in such a way that students are introduced to the various concepts and methodologies at a graduate level. . . . more advanced mathematical details are provided in appendices at the end of the chapters. . . .Cryer and new co-author, Kung-Sik Chan, have compiled a comprehensive resource on time series analysis, integrating traditional time series methodologies with newer techniques and procedures. . . . The first ten chapters deal with time-domain analysis of univariate time series. . . . Deterministic trend models. . . . Autoregressive moving average (ARMA) models. . . . The classic model building approach of Box and Jenkins. . . . including multiclative models. . . . The second part of the book consists of new chapters on more advanced topics. Time series regression models. . . . Models of heteroscedasticity. . . . Frequency-domain analysis. . . . The book concludes with nonlinear time series. . . . The fact that R and the TSA package are freely available . . . contibutes to the accessibility of the book. . . . I would highly recommend this book." (Paul J. van Staden, South African Statistical Association)

“Intended to serve as an introductory course text in time series analysis, this edition is appropriate for a target audience of upper-division undergraduates and beginning graduate students. …The second edition has undergone substantial revision; the most notable changes are the inclusion of new material and the switch from Minitab to the R programming language (R Development Core Team 2008). In fact, the text makes extensive use of the contributed R package TSA, maintained by one of the authors (KSC), providing sample code throughout. It also boasts an appendix containing an introduction to R along with several of the commands use in each chapter. Since many practical problems in time series analysis are solved using statistical software, the change to R will likely be appreciated by students. …This text is well written and provides thorough coverage of univariate ARIMA modeling. In fact, I will strongly consider adopting this text for my next introductory time series class at the advanced undergraduate/beginning graduate level.” ( Journal of the American Statistical Association, Dec. 2009, Vol. 104, No. 488)

“Based on the book on Time Series Analysis by Jonathan Cryer, published in 1968, the new edition, co-authored with K.-S. Chan, contains nearly all of the well-received original in addition to considerable up-to-date new material, numerous new datasets, and new exercises. Hence the book emphasizes the time domain approach and particularly the Box-Jenkins approach. In addition, some of the new topics that are integrated with the original include unit root tests, extended autocorrelation functions, subset ARIMA models, and bootstrapping. Furthermore, the new edition covers completely new chapters on time series regression 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, the discussion is presented in a way that will make the material accessible and quite useful to a broad audience of users. … The book is suitable for a one-semester course 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. The required facts concerning expectation, variance, covariance, correlation, and properties of conditional expectation and minimum mean square error prediction are presented in appendices. … In conclusion, this book is easy to access. It makes the difficult contexts very concrete. Wonderful work and strongly recommended for a graduate course or for self-study.” (Technometrics. August 1, 2010, 52(3), p. 365)

“This second edition…includes new material on time series regression models, spectral analysis, threshold models, and models of heteroscedasticity; the latter of which are heavily used in econometrics and have traditionally been left out of books on time series. The new chapters on heteroscedasticity and threshold models, in my opinion, are what set this book apart from others. … Overall, the book is well laid out and well written. The TSA package easily loaded on my Mac and the software and example code ran without any problems. …I have no reservations recommending it as the text for an applied course, which is the intended use of the book.” ( Biometrics 65, March 2009)

Kurzbeschreibung

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 is assumed through multiple regression. 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 could be selected for inclusion in a course if the instructor so chooses.

Produktinformation

  • Format: Kindle Edition
  • Dateigröße: 9370 KB
  • Seitenzahl der Print-Ausgabe: 491 Seiten
  • Verlag: Springer New York; Auflage: 2 (6. März 2008)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • ASIN: B001Y35GHO
  • Text-to-Speech (Vorlesemodus): Aktiviert
  • X-Ray:
  • Durchschnittliche Kundenbewertung: 4.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)
  • Amazon Bestseller-Rang: #336.944 Bezahlt in Kindle-Shop (Siehe Top 100 Bezahlt in Kindle-Shop)

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0 von 1 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Leichte Schäden 16. Januar 2014
Format:Gebundene Ausgabe|Verifizierter Kauf
Leichte Schäden am Produkt feststellbar. Dies wird durch schnelle Lieferung und den tiefen Preis zum grössten Teil kompensiert. Eigentlich schade, dass keine Hardcover-Version verfügbar ist.
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Amazon.com: 4.2 von 5 Sternen  10 Rezensionen
66 von 67 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen second edition of an excellent text on time series 12. Juni 2008
Von Michael R. Chernick - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
Jonathan Cryer wrote a very nice introductory text on time series analysis in 1986. The book emphasized the time domain approach and particularly the Box-Jenkins approach. There have been many advances in time series analysis over the 22 years that have past since the publication of that book. In this up-to-date edition K.-S. Chan has joined as coauthor. The book now includes such topics as threshold models, time series regression models and outlier detection in time series. Also included for the first time in this edition are the heteroscadastic models including the ARCH and GARCH models. There is also good coverage of frequency domain analysis. An appendix of R commands to do the analyses in the various chapters is also a nice new feature of this edition.
19 von 19 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Excellent Practical Guide 15. November 2008
Von Statistixian - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
Great book. Companion R package TSA is not just another TS Analysis package, but provides excellent additional capabilities, compared to the usual stuff on R. The authors have created a book that's great for self-study or for a graduate course. 5 stars.
14 von 14 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Excellent text for those who want to learn time series on their own. 18. Mai 2010
Von J. Gangolly - Veröffentlicht auf Amazon.com
Format:Taschenbuch|Verifizierter Kauf
Very well written, easy to understand. But it is a basic/elementary textbook. If I were learning time series on my own and wanted to use the R language, I would read this book first.
4 von 4 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen An excellent applied text on time series 5. Januar 2012
Von Brian - Veröffentlicht auf Amazon.com
Format:Taschenbuch
When I was a graduate student in statistics and was faced with a consulting problem involving time series data, one of my professors lent me this book to use as a reference. I must say this is one of the best applied statistics books I have read. The authors explain the concepts well, illustrate the ideas with many different examples, and provide R code so that all results (point estimates and plots) can be recreated. If you are an R user, all the data sets are within their TSA R package. I am not an expert on time series analysis, but when I am faced with time series data, this is the first book I turn to. I just wish all stats. textbooks were written this well.
1 von 1 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen I read and done every exercise in this book 2. Juni 2012
Von raz - Veröffentlicht auf Amazon.com
Format:Taschenbuch
Wonderful book for those folks who are into applied econometrics I like playing with data and testing out concepts this book provides plenty of opportunities to do just that. Anyone interested in Econometrics and R will most likely have a nice time going through this book I did.
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Beliebte Markierungen

 (Was ist das?)
&quote;
principle of parsimony; that is, the model used should require the smallest number of parameters that will adequately represent the time series. &quote;
Markiert von 6 Kindle-Nutzern
&quote;
1. model specification (or identification) 2. model fitting, and 3. model diagnostics &quote;
Markiert von 5 Kindle-Nutzern
&quote;
the probability laws that govern the behavior of the process do not change over time. &quote;
Markiert von 4 Kindle-Nutzern

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