Efficient semiparametric estimation via Cholesky decomposition for longitudinal data
Efficient semiparametric estimation via Cholesky decomposition for longitudinal data
复制标题
DOI：
10.1016/j.csda.2011.06.025
复制
发表时间：
2011-12
期刊：
影响因子：
--
通讯作者：
Ziqi Chen;N. Shi;Wei Gao;M. Tang
中科院分区：
文献类型：
--
作者：
Ziqi Chen;N. Shi;Wei Gao;M. Tang
半参数方法的纵向数据内的依赖科目最近受到了相当大的关注。现有的方法，侧重于建模的均值结构需要一个正确的规范的协方差结构，错误的协方差结构可能会导致效率低下或有偏的均值参数估计。此外，计算和估计问题时出现的重复测量是在不规则的和可能的受试者特定的时间点，协方差矩阵的维数是大的，和正定性的协方差矩阵是必需的。在这篇文章中，我们提出了一个配置文件的核心方法的基础上半参数部分线性回归模型的均值和模型的协方差结构的同时，由修改的Cholesky分解的动机。我们还研究了参数估计的大样本性质。所提出的方法进行评估，通过模拟和应用到一个真实的数据集。理论和实证结果都表明，适当地考虑使用我们的方法的响应之间的主题内的相关性，可以大大提高效率。
Semiparametric methods for longitudinal data with dependence within subjects have recently received considerable attention. Existing approaches that focus on modeling the mean structure require a correct specification of the covariance structure as misspecified covariance structures may lead to inefficient or biased mean parameter estimates. Besides, computation and estimation problems arise when the repeated measurements are taken at irregular and possibly subject-specific time points, the dimension of the covariance matrix is large, and the positive definiteness of the covariance matrix is required. In this article, we propose a profile kernel approach based on semiparametric partially linear regression models for the mean and model covariance structures simultaneously, motivated by the modified Cholesky decomposition. We also study the large-sample properties of the parameter estimates. The proposed method is evaluated through simulation and applied to a real dataset. Both theoretical and empirical results indicate that properly taking into account the within-subject correlation among the responses using our method can substantially improve efficiency.