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    题名: 相關誤差下的迴歸函數適應性估計
    其它题名: Adaptive Estimation in Regression with Dependent Errors
    作者: 黃子銘
    贡献者: 統計學系
    日期: 2013
    上传时间: 2016-04-20 16:52:41 (UTC+8)
    摘要: 無母數迴歸估計問題中，若是迴歸函數平滑程度已知，則容易得到收斂速度爲 最佳的估計量。當迴歸函數平滑程度爲未知時，我們也希望迴歸函數估計量能 達到一樣好的估計效果。這樣的估計量稱爲具有適應性。 當迴歸模型中誤差爲相關時，有人提出基於一種選模規則的迴歸函數估計量， 是具適應性的。然而這方法使用上有困難，因為選模規則牽涉到一些未知參 數。一種可能的解決方式，是將選模規則中的未知參數以其具一致性的估計量 取代。本計畫中將研究此解決方式是否能在理論上證實可行。
    In nonparametric regression, if the degree of smoothness of the regression function is known, it is often easy to obtain estimators that attain the optimal convergence rate. When the degree of smoothness of the regression function is unknown, it is desirable to have estimators for the regression function that can also achieve the optimal convergence rate. Estimators that have this property are called adaptive. When the errors in a regression model are dependent, an adaptive estimator based on a model selection criterion has been proposed, but it is difficult to implement because the criterion involves unknown parameters for the error dependence structure and the error variance. In this project, it is proposed to study the possibility of replacing the unknown parameters by their consistent estimators to make the adaptive estimator implementable.
    關聯: 計畫編號 NSC 102-2118-M004-007
    数据类型: report
    显示于类别:[統計學系] 國科會研究計畫

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    102-2118-M004-007.pdf596KbAdobe PDF2312检视/开启


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