Outlier detection in functional data by depth measures, with application to identify abnormal NOx levels

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Statistical Computing in Functional Data Analysis: The R Package fda.usc (2012, background): “These depth measures are useful to define location and dispersion measures, for classification and for outlier detection (Febrero-Bande, Galeano, and González-Manteiga 2008) (Section 2.6).”
Electricity consumption prediction with functional linear regression using spline estimators (2010, background): “For the first attempts in this direction see, eg, 8 13 .”
Rainbow Plots, Bagplots, and Boxplots for Functional Data (2010, methodology): “Hyndman & Ullah (2007) used a method based on robust principal component analysis, while Febrero et al. (2007, 2008) considered functional outlier detection using successive likelihood ratio tests and smoothed bootstrapping.”