Nonlinear time series
nonparametric and parametric methods
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Author
Contributions
- Yao, Qiwei - Contributor
Publication
2003 - Springer, New York, New York (State)
Language
English
Word Count
137,750 words, Guess
Page Count
551 pages
Identifiers
- Open LibraryOL18141111M
- ISBN-100387951709
- OCLC Control Number50774476
- OCLC Control Numbernonlineartimeser00fanj
- Library of Congress Control Number2002036549
and 2 more
- Goodreads5410648
- LibraryThing4461366
Classifications
- LCCQA280 .F36 2003
Description
This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. A distinct feature of this book is that it applies many modern nonparametric estimation and testing ideas to time series modeling and model identification, while outlines many useful ideas from more traditional time series analysis. This will enable readers to use modern data-analytic techniques while keeping in touch with traditional approaches, and make the book self-contained. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics.
Subjects
Series Statement
- Springer series in statistics
Other Editions
- Nonlinear time series: nonparametric and parametric methods
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