Functional outlier detection and taxonomy by sequential transformations

Wenlin Dai, Tomáš Mrkvička, Ying Sun, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Functional data analysis can be seriously impaired by abnormal observations, which can be classified as either magnitude or shape outliers based on their way of deviating from the bulk of data. Identifying magnitude outliers is relatively easy, while detecting shape outliers is much more challenging. We propose turning the shape outliers into magnitude outliers through data transformation and detecting them using the functional boxplot. Besides easing the detection procedure, applying several transformations sequentially provides a reasonable taxonomy for the flagged outliers. A joint functional ranking, which consists of several transformations, is also defined here. Simulation studies are carried out to evaluate the performance of the proposed method using different functional depth notions. Interesting results are obtained in several practical applications.
Original languageEnglish (US)
Pages (from-to)106960
JournalComputational Statistics and Data Analysis
Volume149
DOIs
StatePublished - Apr 3 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research was supported by the King Abdullah University of Science and Technology (KAUST). Wenlin Dai is also supported by the National Natural Science Foundation of China (Grant No. 11901573).

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