Publikation: Modifying the double smoothing bandwidth selector in nonparametric regression
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In this paper a modified double smoothing bandwidth selector, ^h MDS , based on a new criterion, which combines the plug-in and the double smoothing ideas, is proposed. A self-complete iterative double smoothing rule ( ^ h IDS ) is introduced as a pilot method. The asymptotic properties of both ^ h IDS and ^ h MDS are investigated. It is shown that ^ h MDS performs asymptotically very well. Moreover, it is asymptotically negatively correlated with h ASE , the minimizer of the averaged squared error. The asymptotic performances of ^ h MDS and of the iterative plug-in method, ^ h IPL (Gasser et al., 1991) are compared. A comparative simulation study is carried out to show the practical performance of ^ h MDS and related methods. It is shown that ^ h MDS seems to be the best in the practice. Finite sample negative correlations between the chosen bandwidth selectors and h ASE are also studied.
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BERAN, Jan, Yuanhua FENG, Siegfried HEILER, 2000. Modifying the double smoothing bandwidth selector in nonparametric regressionBibTex
@techreport{Beran2000Modif-650, year={2000}, series={CoFE-Diskussionspapiere / Zentrum für Finanzen und Ökonometrie}, title={Modifying the double smoothing bandwidth selector in nonparametric regression}, number={2000/37}, author={Beran, Jan and Feng, Yuanhua and Heiler, Siegfried} }
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