Partial linear regression models for clustered data
Partial linear regression models for clustered data
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DOI：
10.1198/016214505000000592
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发表时间：
2006-03-01
影响因子：
3.7
通讯作者：
Jin, ZZ
中科院分区：
文献类型：
--
作者：
Chen, K;Jin, ZZ
本文考虑通过偏线性回归模型分析聚类数据。采用广义估计方程方法中的类内相关建模思想，通过对非参数分量的分段局部多项式逼近，得到了斜率参数的最小二乘型估计。这种斜率估计有几个优点：（a）它达到了n（1/2）-一致性，而不欠平滑;（B）当使用正确的群内相关性时，它是有效的，假设误差的多元正态性;（c）无论非参数分量是否是聚类水平，前述性质都保持不变;（d）这种估计方法自然地扩展到处理广义部分线性模型。仿真研究和一个真实的例子支持的理论。
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.