Rainbow Plots, Bagplots, and Boxplots for Functional Data

被引:186
作者
Hyndman, Rob J. [1 ]
Shang, Han Lin [1 ]
机构
[1] Monash Univ, Dept Econometr & Business Stat, Clayton, Vic 3800, Australia
关键词
Highest density regions; Kernel density estimation; Out detection; Robust principal component analysis; Tukey's halfspace location depth; OUTLIER IDENTIFICATION; ESTIMATORS; REGRESSION; LOCATION; DEPTH; MODEL; SHAPE;
D O I
10.1198/jcgs.2009.08158
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We propose new tools for visualizing large amounts of functional data in the form of smooth curves. The proposed tools include functional versions of the bagplot and boxplot, which make use of the first two robust principal component scores. Tukey's data depth and highest density regions. By-products of our graphical displays are outlier detection methods for functional data. We compare these new outlier detection methods with existing methods for detecting outliers in functional data, and show that our methods are better able to identify outliers. An R-package containing computer code and datasets is available in the online supplements.
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页码:29 / 45
页数:17
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