标题： 周期性遥感数据的功能因子分析 显示英文标题

标题： Functional factor analysis for periodic remote sensing data

摘要： 我们提出了一种新的功能数据因子旋转方法。这是通过将功能性主成分旋转到一个预定义的周期函数空间来实现的，该空间旨在将总变异分解为近似周期性和近似非周期性成分，并具有预定义的周期。我们证明，可以通过计算适当空间之间的典型相关性来获得因子旋转，这使得该方法在计算上更加高效。此外，我们展示了在高度复杂的协方差存在的情况下，我们提出的旋转能够提供稳定且可解释的结果。这项工作受到在从遥感获得的植被指数测量的网格时间序列中寻找可解释的变异来源的目标所驱动，并通过对此类数据的因子旋转应用来展示我们的方法。 显示英文摘要

摘要： We present a new approach to factor rotation for functional data. This is achieved by rotating the functional principal components toward a predefined space of periodic functions designed to decompose the total variation into components that are nearly-periodic and nearly-aperiodic with a predefined period. We show that the factor rotation can be obtained by calculation of canonical correlations between appropriate spaces which make the methodology computationally efficient. Moreover, we demonstrate that our proposed rotations provide stable and interpretable results in the presence of highly complex covariance. This work is motivated by the goal of finding interpretable sources of variability in gridded time series of vegetation index measurements obtained from remote sensing, and we demonstrate our methodology through an application of factor rotation of this data.