Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics)

Fan, Jianqing; Yao, Qiwei

ISBN 10: 0387261427 / ISBN 13: 9780387261423

Published by Springer, 2005

About this Item

In English.

Seller Inventory # ria9780387261423_new

Bibliographic Details

• Title: Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics)
• Publisher: Springer
• Publication Date: 2005
• Language: English
• ISBN 10: 0387261427
• ISBN 13: 9780387261423
• Binding: Soft cover
• Edition: 2nd Edition
• Condition: New

About this title

Synopsis

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.

Review

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)