Functional linear mixed models for irregularly or sparsely sampled data
Functional linear mixed models for irregularly or sparsely sampled data
复制标题
DOI：
10.1177/1471082x15617594
复制
发表时间：
2016-02-01
影响因子：
1
通讯作者：
Greven, Sonja
中科院分区：
文献类型：
--
作者：
Cederbaum, Jona;Pouplier, Marianne;Greven, Sonja
我们提出了一种估计方法来分析相关功能数据，这些数据是在不等网格甚至稀疏网格上观察到的。我们使用的模型是函数线性混合模型，是线性混合模型的函数模拟。估计基于通过功能主成分分析和混合模型方法进行降维。我们的程序允许分解数据的变异性以及估计感兴趣的平均效应，并借用曲线的强度。平均效应的置信带可以根据估计的主成分有条件地构建。我们在在线附录中提供了实现我们方法的 R 代码。该方法受到语音生成研究数据的启发并应用于该数据。
We propose an estimation approach to analyse correlated functional data, which are observed on unequal grids or even sparsely. The model we use is a functional linear mixed model, a functional analogue of the linear mixed model. Estimation is based on dimension reduction via functional principal component analysis and on mixed model methodology. Our procedure allows the decomposition of the variability in the data as well as the estimation of mean effects of interest, and borrows strength across curves. Confidence bands for mean effects can be constructed conditionally on estimated principal components. We provide R-code implementing our approach in an online appendix. The method is motivated by and applied to data from speech production research.