Classification Using Censored Functional Data

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Peter Hall, functional data analysis and random objects (2016, background): “…measurements per subject that are made at random locations; Peter’s work included major contributions towards a better understanding of such functional data of “longitudinal type” (Hall, Müller and Wang, 2006; Hall, Müller and Yao, 2008; Delaigle and Hall, 2013), but many open problems remain.”
Functional Data Analysis (2016, background): “Theoretical support and a notion of “perfect classiﬁcation” standing for asymptotically vanishing misclassiﬁcation probabilities has been introduced in Delaigle & Hall (2012) for linear and Delaigle & Hall (2013) for quadratic functional classiﬁcation.”
On the optimal reconstruction of partially observed functional data (2017, background): “This situation corresponds to the case of so-called fragmentary observations, as considered by Delaigle and Hall (2013), Delaigle and Hall (2016), Descary and Panaretos (2018), and Delaigle et al. (2018). To solve this problem we suggest an iterative reconstruction algorithm.”