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Partial linear regression models for clustered data
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DOI：10.1198/016214505000000592.png)
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摘要
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This article considers the analysis of clustered data via partial linear regression models. Adopting the idea of modeling the within-cluster correlation from the method of generalized estimating equations, a least squares type estimate of the slope parameter is obtained through piecewise local polynomial approximation of the nonparametric component. This slope estimate has several advantages: (a) It attains n(1/2)-consistency without undersmoothing; (b) it is efficient when correct within-cluster correlation is used, assuming multivariate normality of the error; (c) the preceding properties hold regardless of whether or not the nonparametric component is of cluster level; and (d) this estimation method naturally extends to deal with generalized partial linear models. Simulation studies and a real example are presented in support of the theory.
Keyword:
asymptotic bias
clustered data
generalized partial linear regression model
mean squared error
nonparametric curve estimation
piecewise local polynomial method
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