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Partial linear regression models for clustered data

Authors Chen, K. View this author's profile
Jin, ZZ
Issue Date 2006
Source Journal of the American Statistical Association , v. 101, (473), 2006, MAR, p. 195-204
Summary 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.
ISSN 0162-1459
Language English
Format Article
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