| 书目名称 | Nonlinear Time Series | | 副标题 | Nonparametric and Pa | | 编辑 | Jianqing Fan,Qiwei Yao | | 视频video | | | 丛书名称 | Springer Series in Statistics | | 图书封面 |  | | 描述 | 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, la | | 出版日期 | Book 2003 | | 关键词 | Time series; econometrics; linear optimization; mathematical statistics; modeling; nonparametric methods; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-0-387-69395-8 | | isbn_softcover | 978-0-387-26142-3 | | isbn_ebook | 978-0-387-69395-8Series ISSN 0172-7397 Series E-ISSN 2197-568X | | issn_series | 0172-7397 | | copyright | Springer-Verlag New York 2003 |
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发表于 2025-3-21 17:32:57
