Efficient estimation in marginal partially linear models for Longitudinal/Clustered data using splines
Efficient estimation in marginal partially linear models for Longitudinal/Clustered data using splines
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DOI：
10.1111/j.1467-9469.2006.00550.x
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发表时间：
2007-09-01
影响因子：
1
通讯作者：
Zhou, Lan
中科院分区：
文献类型：
--
作者：
Huang, Jianhua Z.;Zhang, Liangyue;Zhou, Lan
我们考虑了纵向/聚集数据的边际半参数部分线性模型，并提出了一种基于模型非参数部分的样条逼近和参数边际广义估计方程(GEE)的扩展的估计方法。我们对模型的参数部分和非参数部分的估计都具有与参数GEE类似的性质，即如果正确地指定了协方差结构，则估计是有效的，并且即使错误地指定了协方差结构，估计仍然是一致的和渐近正态的。通过证明我们的估计达到了半参数信息界，我们实际上建立了在比通常考虑的GEE更强的意义上估计模型的参数部分的效率。我们的估计的半参数有效性是通过只假设条件矩限制而不是严格的多元高斯误差假设来获得的。
We consider marginal semiparametric partially linear models for longitudinal/clustered data and propose an estimation procedure based on a spline approximation of the non-parametric part of the model and an extension of the parametric marginal generalized estimating equations (GEE). Our estimates of both parametric part and non-parametric part of the model have properties parallel to those of parametric GEE, that is, the estimates are efficient if the covariance structure is correctly specified and they are still consistent and asymptotically normal even if the covariance structure is misspecified. By showing that our estimate achieves the semiparametric information bound, we actually establish the efficiency of estimating the parametric part of the model in a stronger sense than what is typically considered for GEE. The semiparametric efficiency of our estimate is obtained by assuming only conditional moment restrictions instead of the strict multivariate Gaussian error assumption.