Functional outlier detection and taxonomy by sequential transformations
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Environmental Statistics Group
Spatio-Temporal Statistics and Data Analysis Group
Preprint Posting Date2018-08-16
Online Publication Date2020-04-03
Print Publication Date2020-09
Embargo End Date2022-04-18
Permanent link to this recordhttp://hdl.handle.net/10754/661060
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AbstractFunctional 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.
CitationDai, W., Mrkvička, T., Sun, Y., & Genton, M. G. (2020). Functional outlier detection and taxonomy by sequential transformations. Computational Statistics & Data Analysis, 149, 106960. doi:10.1016/j.csda.2020.106960
SponsorsThis 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).