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• W3015071574 abstract "Nonparametric regression is an alternative to the parametric approach, which consists of applying parametric models, i.e. models of the certain functional form with a fixed number of parameters. As opposed to the parametric approach, nonparametric models have a general form, which can be approximated increasingly precisely when the sample size grows. Hereby they do not impose such restricted assumptions about the form of the modelling dependencies and in consequence, they are more flexible and let the data speak for themselves. That is why they are a promising tool for forecasting, especially in case of nonlinear time series. One of the most popular nonparametric regression method is the Nadaraya- Watson kernel smoothing. Nowadays, there are a number of variations of this method, like the local-linear kernel estimator, which combines the local linear approximation and the kernel estimator. In the paper a Monte Carlo study is conducted in order to assess the usefulness of the kernel smoothers to nonlinear time series forecasting and to compare them with the other techniques of forecasting." @default.
• W3015071574 created "2020-04-10" @default.
• W3015071574 creator A5036373465 @default.
• W3015071574 date "2019-01-30" @default.
• W3015071574 modified "2023-09-27" @default.
• W3015071574 title "Several Aspects of Nonparametric Prediction of Nonlinear Time Series" @default.
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• W3015071574 doi "https://doi.org/10.5604/01.3001.0014.0522" @default.
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