Nonparametric additive regression for repeatedly measured data
Nonparametric additive regression for repeatedly measured data
复制标题
DOI：
10.1093/biomet/asp015
复制
发表时间：
2009-06
影响因子：
2
通讯作者：
R. Carroll;A. Maity;E. Mammen;Kyusang Yu
中科院分区：
文献类型：
--
作者：
R. Carroll;A. Maity;E. Mammen;Kyusang Yu
针对重复测量问题中的加性模型拟合问题，提出了一种易于计算的光滑后拟合算法。我们的方法很容易处理各种设置，例如当一些协变量在重复响应测量中相同时。我们允许回归误差的工作协方差矩阵，表明当使用正确的协方差矩阵时，我们的方法是最有效的。分量函数达到已知的渐近方差的标量参数的情况下的下限。光滑的后拟合也直接导致局部线性情况下的设计无关的偏差。仿真结果表明，我们的估计有较小的方差比通常的核估计。营养流行病学的一个例子也说明了这一点。版权所有2009年，牛津大学出版社。
We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. Copyright 2009, Oxford University Press.