Nonlinear Time Series: Nonparametric and Parametric Methods

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Springer Science & Business Media, Mar 12, 2003 - Business & Economics - 551 pages
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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.
 

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Contents

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Page 521 - Approximation of a-stable probability densities using finite Gaussian mixtures," Proc. EUSIPCO'98 , Sep. 1998. 12. JP Nolan, "Multivariate stable distributions: approximation, estimation, simulation and identification," in A Practical Guide to Heavy Tails, RJ Adler, RE Feldman, and MS Taqqu, eds., Boston: Birkhauser, 1998.

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About the author (2003)

Jianqing Fan, coauthor of the highly regarded book Local Polynomial Modeling, is Professor of Statistics and Financial Econometrics at Princeton University and former Professor of Statistics at the University of North Carolina at Chapel Hill and the Chinese University of Hong Kong. His published work on nonparametric modeling, nonlinear time series, financial economietrics, analysis of longitudinal data, model selection, wavelets, and other aspects of methodological and theoretical statistics has been recognized with the Presidents' Award from the Committee of Presidents of Statistical Societies, with the Hettleman Prize for Artistic and Scholarly Achievement from the University of North Carolina, and by his election as a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Qiwei Yao is Professor of Statistics at the London School of Economics and Political Science. He is an elected member of the International Statistical Institute, and has served on the editorial boards for the Journal of the Royal Statistical Society (Series B) and the Australian and New Zealand Journal of Statistics

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