頁籤選單縮合
題 名 | Robust Local Polynomial Regression for Dependent Data |
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作 者 | Jiang,Jiancheng; Mack,Y. P.; | 書刊名 | Statistica Sinica |
卷 期 | 11:3 2001.07[民90.07] |
頁 次 | 頁705-722 |
分類號 | 319.22 |
關鍵詞 | Data-driven; Local M-estimator; Local polynomial regression; Mixing condition; One-step; Robustness; |
語 文 | 英文(English) |
英文摘要 | Let (Xj, Yj)nj=1 be a realization of a bivariate jointly strictly strictly stationary process. We consider a robust estimator of the regression function m(x)=E(Y│X=x) by using local polynomial regression techniques. The estimator is a local M-estimator weighted by a kernel function. Under mixing conditions satisfied by many time series models, together with other appropriate conditions, consistency and asymptotic normality results are established. One-step local M-estimators are introduced to reduce computational burden. In addition, we give a data-driven choice for minimizing the scale factor involving the ψ-function in the asymptotic covariance expression, by drawing a parallel with the class of Huber’s ψ-functions. The method is illustrated via two examples. |
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