Nonlinear Time Series Semiparametric and Nonparametric Methods
During the past two decades or so, there has been a lot of interest in both theoretical and empirical analysis of nonlinear time series data. Models and methods used have been based initially on parametric nonlinear or nonparametric time series models. Such parametric nonlinear models and relate...
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| Tác giả chính: | |
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| Định dạng: | Sách |
| Ngôn ngữ: | English |
| Được phát hành: |
Chapman & Hall
2012
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| Những chủ đề: | |
| Truy cập trực tuyến: | http://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/30554 |
| Các nhãn: |
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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| Tóm tắt: | During the past two decades or so, there has been a lot of interest in
both theoretical and empirical analysis of nonlinear time series data.
Models and methods used have been based initially on parametric nonlinear
or nonparametric time series models. Such parametric nonlinear
models and related methods may be too restrictive in many cases. This
leads to various nonparametric techniques being used to model nonlinear
time series data. The main advantage of using nonparametric methods
is that the data may be allowed to speak for themselves in the sense of
determining the form of mathematical relationships between time series
variables. In modelling nonlinear time series data one of the tasks is to
study the structural relationship between the present observation and
the history of the data set. |
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