Rob J Hyndman and Han Lin Shang
Journal of Computational and Graphical Statistics (2010), 19(1), 29–45.
Abstract: We propose new tools for visualizing large numbers of functional data in the form of smooth curves or surfaces. The proposed tools include functional versions of the bagplot and boxplot, and 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 the outliers.
Keywords: Highest density regions, Robust principal component analysis, Kernel density estimation, Outlier detection, Tukey’s halfspace depth.