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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/110301


    Title: Reducing Variance in Univariate Smoothing
    Authors: Cheng, Ming-Yen;Peng, Liang;Wu, Jyh-Shyang
    Keywords: Bandwidth;coverage probability;kernel;local linear regression;nonparametric smoothing;variance reduction
    Date: 2007-04-15
    Issue Date: 2017-05-18 02:10:23 (UTC+8)
    Abstract: A variance reduction technique in nonparametric smoothing is proposed: at each point of estimation, form a linear combination of a preliminary estimator evaluated at nearby points with the coefficients specified so that the asymptotic bias remains unchanged. The nearby points are chosen to maximize the variance reduction. We study in detail the case of univariate local linear regression. While the new estimator retains many advantages of the local linear estimator, it has appealing asymptotic relative efficiencies. Bandwidth selection rules are available by a simple constant factor adjustment of those for local linear estimation. A simulation study indicates that the finite sample relative efficiency often matches the asymptotic relative efficiency for moderate sample sizes. This technique is very general and has a wide range of applications.
    Relation: The Annals of Statistics 35(2), pp.522-542
    DOI: 10.1214/009053606000001398
    Appears in Collections:[數學學系暨研究所] 期刊論文

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