Publication:
Integrated Depths for Partially Observed Functional Data

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2022-05-01
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Taylor & Francis
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Abstract
Partially observed functional data are frequently encountered in applications and are the object of an increasing interest by the literature. We here address the problem of measuring the centrality of a datum in a partially observed functional sample. We propose an integrated functional depth for partially observed functional data, dealing with the very challenging case where partial observability can occur systematically on any observation of the functional dataset. In particular, differently from many techniques for partially observed functional data, we do not request that some functional datum is fully observed, nor we require that a common domain exist, where all of the functional data are recorded. Because of this, our proposal can also be used in those frequent situations where reconstructions methods and other techniques for partially observed functional data are inapplicable. By means of simulation studies, we demonstrate the very good performances of the proposed depth on finite samples. Our proposal enables the use of benchmark methods based on depths, originally introduced for fully observed data, in the case of partially observed functional data. This includes the functional boxplot, the outliergram and the depth versus depth classifiers. We illustrate our proposal on two case studies, the first concerning a problem of outlier detection in German electricity supply functions, the second regarding a classification problem with data obtained from medical imaging. Supplementary materials for this article are available online.
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classification of partially observed funcional data, funcitonal boxplot, functional depth, functional outliers, incomplete functional data, robustnes
Bibliographic citation
Antonio Elías, Raúl Jiménez, Anna M. Paganoni & Laura M. Sangalli (2023) Integrated Depths for Partially Observed Functional Data, Journal of Computational and Graphical Statistics, 32:2, 341-352, DOI: 10.1080/10618600.2022.2070171