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    Functional outlier detection and taxonomy by sequential transformations

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    Name:
    1808.05414.pdf
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    2.267Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Dai, Wenlin
    Mrkvička, Tomáš
    Sun, Ying cc
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Environmental Statistics Group
    Spatio-Temporal Statistics and Data Analysis Group
    Statistics Program
    Date
    2020-04-03
    Preprint Posting Date
    2018-08-16
    Online Publication Date
    2020-04-03
    Print Publication Date
    2020-09
    Embargo End Date
    2022-04-18
    Submitted Date
    2019-06-30
    Permanent link to this record
    http://hdl.handle.net/10754/661060
    
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    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.
    Citation
    Dai, 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
    Sponsors
    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).
    Publisher
    Elsevier BV
    Journal
    Computational Statistics & Data Analysis
    DOI
    10.1016/j.csda.2020.106960
    arXiv
    1808.05414
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0167947320300517
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.csda.2020.106960
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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