Plug-in method for nonparametric regression |
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Authors: | Jan Koláček |
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Institution: | (1) Faculty of Science, Masaryk University, Janackovo nam. 2a, Brno, Czech Republic |
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Abstract: | The problem of bandwidth selection for non-parametric kernel regression is considered. We will follow the Nadaraya–Watson
and local linear estimator especially. The circular design is assumed in this work to avoid the difficulties caused by boundary
effects. Most of bandwidth selectors are based on the residual sum of squares (RSS). It is often observed in simulation studies
that these selectors are biased toward undersmoothing. This leads to consideration of a procedure which stabilizes the RSS
by modifying the periodogram of the observations. As a result of this procedure, we obtain an estimation of unknown parameters
of average mean square error function (AMSE). This process is known as a plug-in method. Simulation studies suggest that the
plug-in method could have preferable properties to the classical one.
Supported by the MSMT: LC 06024. |
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Keywords: | Bandwidth selection Fourier transform Kernel estimation Nonparametric regression |
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