论文标题
稀疏观察到的配对功能数据的稳健关节建模
Robust joint modeling of sparsely observed paired functional data
论文作者
论文摘要
开发了降低的混合效应模型，用于稀疏观察到的配对功能数据的鲁棒建模。在此模型中，使用一些功能主组件汇总了每个功能变量的曲线，并且两个功能变量的关联是通过主组分分数的关联来建模的。正常分布的多元尺度混合物用于对主成分得分和测量误差进行建模，以处理外观观察并实现强大的推断。平均功能和主成分函数是使用花键建模的，并应用粗糙度惩罚以避免过度拟合。 EM算法是用于计算模型拟合和预测的。一项仿真研究表明，所提出的方法的表现优于现有方法，该方法不是为了可靠的估计而设计的。在拟合IA型超新星的多带光曲线的应用中，说明了所提出的方法的有效性。
A reduced-rank mixed effects model is developed for robust modeling of sparsely observed paired functional data. In this model, the curves for each functional variable are summarized using a few functional principal components, and the association of the two functional variables is modeled through the association of the principal component scores. Multivariate scale mixture of normal distributions is used to model the principal component scores and the measurement errors in order to handle outlying observations and achieve robust inference. The mean functions and principal component functions are modeled using splines and roughness penalties are applied to avoid overfitting. An EM algorithm is developed for computation of model fitting and prediction. A simulation study shows that the proposed method outperforms an existing method which is not designed for robust estimation. The effectiveness of the proposed method is illustrated in an application of fitting multi-band light curves of Type Ia supernovae.
