頁籤選單縮合
題 名 | Design-Adaptive Minimax Local Linear Regression for Longitudinal/Clustered Data |
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作 者 | Chen, Kani; Fan, Jianqing; Jin, Zhezhen; | 書刊名 | Statistica Sinica |
卷 期 | 18:2 2008.04[民97.04] |
頁 次 | 頁515-534 |
分類號 | 319 |
關鍵詞 | Asymptotic bias; Generalized estimating equations; Kernel function; Linear minimax efficiency; Mean squared error; Nonparametric curve estimation; |
語 文 | 英文(English) |
英文摘要 | This paper studies a weighted local linear regression smoother for longitudinal/clustered data, which takes a form similar to the classical weighted least squares estimate. As a hybrid of the methods of Chen and Jin (2005) and Wang (2003), the proposed local linear smoother maintains the advantages of both methods in computational and theoretical simplicity, variance minimization and bias reduction. Moreover, the proposed smoother is optimal in the sense that it attains linear minimax efficiency when the within-cluster correlation is correctly specified. In the special case that the joint density of covariates in a cluster exists and is continuous, any working within-cluster correlation would lead to linear minimax efficiency for the proposed method. |
本系統中英文摘要資訊取自各篇刊載內容。