A plug-in technique in nonparametric regression with dependence |
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| Authors: | Alejandro Quintela del Río |
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| Affiliation: | Facultad de Informática , Universidad de La Coru?a , Campus de Elvi?a, s/n, La Coru?a, SPAIN |
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| Abstract: | The problem addressed is that of smoothing parameter selection in kernel nonparametric regression in the fixed design regression model with dependent noise. An asymptotic expression of the optimum bandwidth parameter has been obtained in recent studies, where this takes the form h = C 0 n ?1/5. This paper proposes to use a plug-in methodology, in order to obtain an optimum estimation of the bandwidth parameter, through preliminary estimation of the unknown value of C 0. |
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| Keywords: | nonparametric regression kernel estimate bandwidth selection plug-in strongly mixing processes cross-validation asymptotic normality time series |
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