We have a dedicated site for Germany. Authors: Fan , Jianqing, Yao , Qiwei. 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 s at least, there still exists healthy and justi?
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. 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? Beyond postulated parametric models, there are in?
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- Nonlinear Time Series Nonparametric and Parametric Methods.
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Springer Series in Statistics
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8 editions of this work
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Related Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics)
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