Efficient semiparametric regression for longitudinal data with nonparametric covariance estimation
Efficient semiparametric regression for longitudinal data with nonparametric covariance estimation
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DOI：
10.1093/biomet/asq080
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发表时间：
2011-06-01
期刊：
影响因子：
2.7
通讯作者：
Li, Yehua
中科院分区：
文献类型：
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作者：
Li, Yehua
对于纵向数据，当受试者内协方差被错误指定时，半参数回归估计可能是低效的。我们提出了一种有效的半参数估计和非参数协方差估计相结合的方法，并且对协方差模型的错误指定具有很强的鲁棒性。我们证明了核协方差估计为受试者内协方差矩阵提供了一致一致的估计，并且替换了非参数协方差的半参数剖面估计仍然是半参数有效的。通过仿真验证了该估计器的有限样本性能。在对艾滋病临床试验中的CD4计数数据的应用中，我们将所提出的方法扩展到协方差模型的函数分析。
For longitudinal data, when the within-subject covariance is misspecified, the semiparametric regression estimator may be inefficient. We propose a method that combines the efficient semiparametric estimator with nonparametric covariance estimation, and is robust against misspecification of covariance models. We show that kernel covariance estimation provides uniformly consistent estimators for the within-subject covariance matrices, and the semiparametric profile estimator with substituted nonparametric covariance is still semiparametrically efficient. The finite sample performance of the proposed estimator is illustrated by simulation. In an application to CD4 count data from an AIDS clinical trial, we extend the proposed method to a functional analysis of the covariance model.