Nonlinear Time Series: Nonparametric and Parametric Methods:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
2003
|
| Series: | Springer Series in Statistics
|
| Subjects: | |
| Online Access: | Volltext |
| Item Description: | 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 |
| Physical Description: | 1 Online-Ressource (XIX, 553 p) |
| ISBN: | 9780387224329 9780387951706 |
| DOI: | 10.1007/b97702 |
Staff View
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| language | English |
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| spelling | Fan, Jianqing Verfasser aut Nonlinear Time Series: Nonparametric and Parametric Methods edited by Jianqing Fan, Qiwei Yao New York, NY Springer New York 2003 1 Online-Ressource (XIX, 553 p) txt rdacontent c rdamedia cr rdacarrier 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 Statistics Finance Mathematical statistics Econometrics Statistical Theory and Methods Quantitative Finance Statistik Nichtlineare Zeitreihenanalyse (DE-588)4276267-4 gnd rswk-swf Nichtparametrisches Verfahren (DE-588)4339273-8 gnd rswk-swf Parametrisches Verfahren (DE-588)4205938-0 gnd rswk-swf Nichtlineare Zeitreihenanalyse (DE-588)4276267-4 s Parametrisches Verfahren (DE-588)4205938-0 s 1\p DE-604 Nichtparametrisches Verfahren (DE-588)4339273-8 s 2\p DE-604 Yao, Qiwei Sonstige oth https://doi.org/10.1007/b97702 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Fan, Jianqing Nonlinear Time Series: Nonparametric and Parametric Methods Statistics Finance Mathematical statistics Econometrics Statistical Theory and Methods Quantitative Finance Statistik Nichtlineare Zeitreihenanalyse (DE-588)4276267-4 gnd Nichtparametrisches Verfahren (DE-588)4339273-8 gnd Parametrisches Verfahren (DE-588)4205938-0 gnd |
| subject_GND | (DE-588)4276267-4 (DE-588)4339273-8 (DE-588)4205938-0 |
| title | Nonlinear Time Series: Nonparametric and Parametric Methods |
| title_auth | Nonlinear Time Series: Nonparametric and Parametric Methods |
| title_exact_search | Nonlinear Time Series: Nonparametric and Parametric Methods |
| title_full | Nonlinear Time Series: Nonparametric and Parametric Methods edited by Jianqing Fan, Qiwei Yao |
| title_fullStr | Nonlinear Time Series: Nonparametric and Parametric Methods edited by Jianqing Fan, Qiwei Yao |
| title_full_unstemmed | Nonlinear Time Series: Nonparametric and Parametric Methods edited by Jianqing Fan, Qiwei Yao |
| title_short | Nonlinear Time Series: Nonparametric and Parametric Methods |
| title_sort | nonlinear time series nonparametric and parametric methods |
| topic | Statistics Finance Mathematical statistics Econometrics Statistical Theory and Methods Quantitative Finance Statistik Nichtlineare Zeitreihenanalyse (DE-588)4276267-4 gnd Nichtparametrisches Verfahren (DE-588)4339273-8 gnd Parametrisches Verfahren (DE-588)4205938-0 gnd |
| topic_facet | Statistics Finance Mathematical statistics Econometrics Statistical Theory and Methods Quantitative Finance Statistik Nichtlineare Zeitreihenanalyse Nichtparametrisches Verfahren Parametrisches Verfahren |
| url | https://doi.org/10.1007/b97702 |
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