Varying-coefficient models and basis function approximations for the analysis of repeated measurements
Varying-coefficient models and basis function approximations for the analysis of repeated measurements
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DOI：
10.1093/biomet/89.1.111
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发表时间：
2002-03-01
期刊：
影响因子：
2.7
通讯作者：
Zhou, L
中科院分区：
文献类型：
--
作者：
Huang, JHZ;Wu, CO;Zhou, L
本文提出了一种用基函数近似估计变系数模型参数的全局平滑方法。基于一个rescent主题引导的推理程序，提出了构建置信区域，并进行假设检验。我们的估计量的条件偏差和方差及其渐近一致性显式开发。我们的程序的有限样本性质进行了研究，通过模拟研究。所提出的方法的应用演示通过一个例子在流行病学。与现有的方法相比，这种方法适用于是否协变量是时不变的，并且不需要分箱的数据时，观察稀疏在不同的观察时间。
A global smoothing procedure is developed using basis function approximations for estimating the parameters of a varying-coefficient model with repeated measurements. Inference procedures based on a resampling subject bootstrap are proposed to construct confidence regions and to perform hypothesis testing. Conditional biases and variances of our estimators and their asymptotic consistency are developed explicitly. Finite sample properties of our procedures are investigated through a simulation study. Application of the proposed approach is demonstrated through an example in epidemiology. In contrast to the existing methods, this approach applies whether or not the covariates are time-invariant and does not require binning of the data when observations are sparse at distinct observation times.