Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/45627

Design-adaptive minimax local linear regression for longitudinal/clustered data

Authors Chen, Kani View this author's profile
Fan, Jianqing
Jin, Zhezhen
Issue Date 2008
Source Statistica Sinica , v. 18, (2), 2008, APR, p. 515-534
Summary 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.
Subjects
ISSN 1017-0405
Language English
Format Article
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