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dc.contributor.authorDai, Wenlin
dc.contributor.authorMrkvička, Tomáš
dc.contributor.authorSun, Ying
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2020-01-16T13:02:34Z
dc.date.available2020-01-16T13:02:34Z
dc.date.issued2020-04-03
dc.date.submitted2019-06-30
dc.identifier.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
dc.identifier.issn0167-9473
dc.identifier.doi10.1016/j.csda.2020.106960
dc.identifier.urihttp://hdl.handle.net/10754/661060
dc.description.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.
dc.description.sponsorshipThis 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).
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0167947320300517
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics and Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics and Data Analysis, [149, , (2020-04-03)] DOI: 10.1016/j.csda.2020.106960 . © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleFunctional outlier detection and taxonomy by sequential transformations
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Group
dc.contributor.departmentStatistics Program
dc.identifier.journalComputational Statistics and Data Analysis
dc.rights.embargodate2022-04-18
dc.eprint.versionPre-print
dc.contributor.institutionInstitute of Statistics and Big Data, Renmin University of China, Beijing 100872, China
dc.contributor.institutionDepartment of Applied Mathematics and Informatics, Faculty of Economics, University of South Bohemia, Studentská 13, 37005 České Budějovice, Czech Republic
dc.identifier.volume149
dc.identifier.pages106960
dc.identifier.arxivid1808.05414
kaust.personSun, Ying
kaust.personGenton, Marc G.
dc.date.accepted2020-03-19
dc.identifier.eid2-s2.0-85083312381
refterms.dateFOA2020-01-16T13:03:23Z
dc.date.published-online2020-04-03
dc.date.published-print2020-09
dc.date.posted2018-08-16


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