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• W2104366165 abstract "This paper studies a weighted local linear regression smoother for longi- tudinal/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 meth- ods 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." @default.
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• W2104366165 date "2008-04-01" @default.
• W2104366165 modified "2023-09-26" @default.
• W2104366165 title "Design-adaptive minimax local linear regression for longitudinal/clustered data" @default.
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