Incorporating covariates in skewed functional data models.
Incorporating covariates in skewed functional data models.
复制标题
将协变量纳入倾斜的函数数据模型中。
DOI：
10.1093/biostatistics/kxu055
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Bondell,HowardD
中科院分区：
文献类型：
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作者：
Li,Meng;Staicu,Ana-Maria;Bondell,HowardD
我们介绍了一类协变量调整偏态函数模型（cSFM）设计的功能数据表现出位置依赖的边缘分布。我们提出了一个半参数Copula模型的逐点边际分布，这是允许依赖于协变量，和功能的依赖，这是假设协变量不变。所提出的cSFM框架为逐点分位数估计和轨迹预测提供了一个统一的平台。我们认为一个计算上可行的程序，处理密集以及稀疏观察的功能数据。该方法进行了检查数值模拟，并适用于一个新的纤维束成像研究多发性硬化症。此外，该方法在RpackagecSFM中实现，该方法在CRAN上公开。
We introduce a class of covariate-adjusted skewed functional models (cSFM) designed for functional data exhibiting location-dependent marginal distributions. We propose a semi-parametric copula model for the pointwise marginal distributions, which are allowed to depend on covariates, and the functional dependence, which is assumed covariate invariant. The proposed cSFM framework provides a unifying platform for pointwise quantile estimation and trajectory prediction. We consider a computationally feasible procedure that handles densely as well as sparsely observed functional data. The methods are examined numerically using simulations and is applied to a new tractography study of multiple sclerosis. Furthermore, the methodology is implemented in theRpackagecSFM, which is publicly available on CRAN.