论文标题
功能数据的强大平滑规范相关分析
Robust smoothed canonical correlation analysis for functional data
论文作者
论文摘要
本文通过使用稳健的关联和比例度量结合基础扩展和/或惩罚作为正则化工具，为希尔伯特可分离空间上的第一个规范相关性和随机元素的第一个规范相关性和方向提供了强大的估计量。在规律性条件下，最终的估计器是一致的。
This paper provides robust estimators for the first canonical correlation and directions of random elements on Hilbert separable spaces by using robust association and scale measures combined with basis expansion and/or penalizations as a regularization tool. Under regularity conditions, the resulting estimators are consistent.
