Maximal autocorrelation functions in functional data analysis

被引：0
作者：
Giles Hooker
Steven Roberts
机构：
[1] Cornell University,Department of Biological Statistics and Computational Biology
[2] Australian National University,Research School of Finance, Actuarial Studies and Applied Statistics
来源：
Statistics and Computing | 2016年 / 26卷
关键词：
Factor rotation; Functional data; Interpretability ; Principal components analysis;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper proposes a new factor rotation for the context of functional principal components analysis. This rotation seeks to re-express a functional subspace in terms of directions of decreasing smoothness as represented by a generalized smoothing metric. The rotation can be implemented simply and we show on two examples that this rotation can improve the interpretability of the leading components.
引用
收藏
页码：945 / 950
页数：5
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