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＜電子ブック＞
Nonlinear Time Series : Nonparametric and Parametric Methods / by Jianqing Fan, Qiwei Yao
(Springer Series in Statistics. ISSN:2197568X)

版	1st ed. 2003.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2003
大きさ	XX, 552 p : online resource
著者標目	*Fan, Jianqing author Yao, Qiwei author SpringerLink (Online service)
件　名	LCSH:Probabilities LCSH:Statistics  LCSH:Social sciences—Mathematics LCSH:Econometrics FREE:Probability Theory FREE:Statistical Theory and Methods FREE:Mathematics in Business, Economics and Finance FREE:Econometrics
一般注記	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 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 HTTP:URL=https://doi.org/10.1007/978-0-387-69395-8

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