Nonlinear Time Series

Nonparametric and Parametric Methods

Jianqing Fan, Qiwei Yao

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English

Springer Verlag
01 November 2005

Mathematics & Sciences; Probability & statistics

Series: Springer Series in Statistics

Amongmanyexcitingdevelopmentsinstatisticsoverthelasttwodecades, nonlineartimeseriesanddata-analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In spite of the fact that the - plication of nonparametric techniques in time series can be traced back to the 1940s at least, there still exists healthy and justi?ed skepticism about the capability of nonparametric methods in time series analysis. As - thusiastic explorers of the modern nonparametric toolkit, we feel obliged to assemble together in one place the newly developed relevant techniques. Theaimofthisbookistoadvocatethosemodernnonparametrictechniques that have proven useful for analyzing real time series data, and to provoke further research in both methodology and theory for nonparametric time series analysis. Modern computers and the information age bring us opportunities with challenges. Technological inventions have led to the explosion in data c- lection (e.g., daily grocery sales, stock market trading, microarray data). The Internet makes big data warehouses readily accessible. Although cl- sic parametric models, which postulate global structures for underlying systems, are still very useful, large data sets prompt the search for more re?nedstructures,whichleadstobetterunderstandingandapproximations of the real world. Beyond postulated parametric models, there are in?nite other possibilities. Nonparametric techniques provide useful exploratory tools for this venture, including the suggestion of new parametric models and the validation of existing ones.

By: Jianqing Fan, Qiwei Yao
Imprint: Springer Verlag
Country of Publication: United States
Dimensions: Height: 235mm, Width: 155mm, Spine: 29mm
Weight: 1.760kg
ISBN: 9780387261423
ISBN 10: 0387261427
Series: Springer Series in Statistics
Publication Date: 01 November 2005
Audience: College/higher education , A / AS level , Further / Higher Education
Format: Paperback
Publisher's Status: Active

Characteristics of Time Series.- ARMA Modeling and Forecasting.- Parametric Nonlinear Time Series Models.- Nonparametric Density Estimation.- Smoothing in Time Series.- Spectral Density Estimation and Its Applications.- Nonparametric Models.- Model Validation.- Nonlinear Prediction.

Reviews for Nonlinear Time Series: Nonparametric and Parametric Methods

From the reviews: The book will particularly appeal to those in the economic sciences and financial engineering who have a solid background in linear time series models and methods. ... I would recommend it to postgraduate students who are interested in learning about recent developments in non-linear and non-parametric time series modelling as well as in understanding the use of complex parametric non-linear and non-parametric time series models in practice. (Jiti Gao, Australian Journal of Agricultural and Resource Economics, Vol. 49, 2005) ...the authors should be congratulated for writing a coherent monograph on modern time series analysis with a focus on nonparametric approaches. I believe that this book will become a standard reference in this area and remain so for a long time. Graduate students in statistics, economics, and financial engineering should be happy to have a much-needed textbook on modern time series methods, which covers not only ARIMA models, but also the newer and more flexible nonlinear and nonparametric techniques. Technometrics, February 2004 This is a book that one can read as a beginner or as an expert. Although there are plenty of theorems, there are also plenty of numerical examples, with both real and simulated data, and lots of pictures and graphics (SPLUS-style). The topics are very fully explained and discussed, and there are many pointers to the literature for further study (with about six hundred references listed). ISI Short Book Reviews, Vol. 24/1, Apr. 2004 Fan and Yao's book has a lot to offer. First, it is readable, even by those with limited knowledge of time-series analysis, as the authors spend time on all the basic concepts. Second, it is self-contained so you do not need other books to understand it. Third, it contains many examples and illustrations to explain the intuition behind the concepts. Fourth, it is up to date and has the latest cutting-edge methods to handle nonlinear time series. Quantitative Finance, 2004