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    題名: Integrated cross-validation for the random design nonparametric regression
    作者: Chang, Tzu-Kuei;Deng, Wen-Shuenn;Lin, Jung-Huei;Chu, C. K.
    貢獻者: 淡江大學統計學系
    關鍵詞: bandwidth selection;cross-validation;integrated cross-validation;nonparametric regression;sparse design;unweighted integrated square error;weighted integrated square error
    日期: 2005-03-01
    上傳時間: 2009-11-30 12:53:38 (UTC+8)
    出版者: 中華民國數學會
    摘要: For the random design nonparametric regression, cross-validation is a popular bandwidth selector. It is constructed by using the criterion of ``weighted" integrated square error. In practice, however, the weighting scheme by the design density in the criterion causes that its associated cross-validation function puts more emphasis in regions with more data, gives little attention to regions with few data, but has no consideration for regions without data. In such a case, the value of the cross-validated bandwidth depends on the distribution of the design points, but is independent of the location of the interval on which the regression function value is estimated. Hence, if there are sparse regions in the realization of the design, then the resulting cross-validated bandwidth is usually not large enough in magnitude such that its corresponding kernel regression function estimate has rough appearance in these sparse regions. To avoid this drawback to cross-validation, we suggest using the criterion of ''unweighted'' integrated square error to construct the bandwidth selector. Under the criterion, a bandwidth selector called integrated cross-validation is proposed, and the resulting bandwidth is shown to be asymptotically optimal. Empirical studies demonstrate that the kernel regression function estimate obtained by using our proposed bandwidth is better than that employing the ordinary cross-validated bandwidth, in both senses of having smoother appearance and yielding smaller sample unweighted integrated square error.
    關聯: Taiwanese journal of mathematics 9(1), pp.123-141
    顯示於類別:[統計學系暨研究所] 期刊論文

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