頁籤選單縮合
| 題 名 | A Variation on Local Linear Regression |
|---|---|
| 作 者 | Jones,M. C.; | 書刊名 | Statistica Sinica |
| 卷 期 | 7:4 1997.10[民86.10] |
| 頁 次 | 頁1171-1180 |
| 分類號 | 319.51 |
| 關鍵詞 | Binning; Boundary correction; Kernel smoothing; Nonparametric regression; |
| 語 文 | 英文(English) |
| 英文摘要 | There has been much justifiable recent interest in local polynomial regression, and in particular in its local linear special case. Local linear regression has advantages in terms of desirable theoretical properties both in the interior and near the boundaries of the region of interest. For implementation, binning is useful. In this paper, we describe a variation on local linear regression which can be considered an alternative binning thereof. We show that existing and novel methods are almost indistinguishable. The point of the paper is not to extol the virtues of the new version over the old, but rather (i) to show that the good properties of local linear regression can be achieved in more than one way, and (ii) to elucidate close links between local linear regression and other kernel smoothing methods. The latter include, most closely, a boundary corrected 'naïve' kernel estimator and a recent proposal of Wu and Chu (1992), as well as binned Nadaraya-Watson estimators and methods for binomial regression. |
本系統中英文摘要資訊取自各篇刊載內容。