Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics)


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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?

Nonparametric techniques provide useful exploratory tools for this venture, including the suggestion of new parametric models and the validation of existing ones. JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. Mathematics Probability Theory and Stochastic Processes. Springer Series in Statistics Free Preview. Buy eBook. Nonparametric Regression and Spline Smoothing. Testing goodness-of-fit in regres- sion via order selection.


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Springer Series in Statistics

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Differences between Parametric and Non-Parametric Methods in machine learning

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8 editions of this work

Bootstrap of kernel smoothing in nonlinear time series. Bernoulli 8, 1— Franses, P. Cambridge University Press. Frisch, U. Galka, A. World Scientific, Singapore. Gallant, A. On the bias in flexible functional forms and an essentially unbiased form: the Fourier flexible form. Journal of Econometrics 15, — On the asymptotic normality of Fourier flexible form estimates. Journal of Econometrics 50, — Gao, J. Semiparametric regression modelling of nonlinear time series. A semiparametric approach to pricing interest rate derivative securities.

Modelling long—range dependent Gaussian processes with application in continuous—time financial models.

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Asymptotic normality of pseudo-LS estimator for partially linear autoregressive models. Statistical inference in single-index and partially nonlinear regression models. Annals of the Institute of Sta- tistical Mathematics 49, — Estimation in semiparametric spatial regression. Annals of Statistics 36, — M-type smoothing splines in nonparametric and semiparametric regression models. Statistica Sinica 7, — Semiparametric nonlinear time series model selection. Nonparametric and semiparametric regres- sion model selection. Unpublished technical report. Adaptive orthogonal series estimation in additive stochastic regression models.

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Jianqing Fan - Citations Google Scholar

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Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics) Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics)
Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics) Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics)
Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics) Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics)
Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics) Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics)
Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics) Nonlinear Time Series: Nonparametric and Parametric Methods (Springer Series in Statistics)

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