Generalized functional additive mixed models
Generalized functional additive mixed models
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DOI：
10.1214/16-ejs1145
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发表时间：
2016-01-01
影响因子：
1.1
通讯作者：
Greven, Sonja
中科院分区：
文献类型：
--
作者：
Scheipl, Fabian;Gertheiss, Jan;Greven, Sonja
我们提出了一个全面的框架，用于非高斯函数响应的加性回归模型，允许多个（部分）嵌套或交叉函数随机效应，具有灵活的相关结构，例如，空间、时间或纵向功能数据，以及功能和标量协变量的线性和非线性效应，这些协变量可能在功能反应指数上平滑变化。我们的实现可以处理任何指数族分布以及许多其他分布（如Beta分布或缩放和移位t分布）的函数响应。发展的动机和评价上的应用程序，以大规模的纵向饲养记录的猪。在广泛的模拟研究以及复制的两个以前发表的模拟研究广义功能混合模型的结果表明，我们的建议的良好性能。该方法是在有据可查的开源软件中实现的，在R-package退款中的pffr函数。
We propose a comprehensive framework for additive regression models for non-Gaussian functional responses, allowing for multiple (partially) nested or crossed functional random effects with flexible correlation structures for, e.g., spatial, temporal, or longitudinal functional data as well as linear and nonlinear effects of functional and scalar covariates that may vary smoothly over the index of the functional response. Our implementation handles functional responses from any exponential family distribution as well as many others like Beta- or scaled and shifted t-distributions. Development is motivated by and evaluated on an application to large-scale longitudinal feeding records of pigs. Results in extensive simulation studies as well as replications of two previously published simulation studies for generalized functional mixed models demonstrate the good performance of our proposal. The approach is implemented in well-documented open source software in the pffr function in R-package refund.