Functional outlier detection and taxonomy by sequential transformations
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionEnvironmental Statistics Group
Spatio-Temporal Statistics and Data Analysis Group
Statistics Program
Date
2020-04-03Preprint Posting Date
2018-08-16Online Publication Date
2020-04-03Print Publication Date
2020-09Embargo End Date
2022-04-18Submitted Date
2019-06-30Permanent link to this record
http://hdl.handle.net/10754/661060
Metadata
Show full item recordAbstract
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.2020.106960Sponsors
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 BVarXiv
1808.05414Additional Links
https://linkinghub.elsevier.com/retrieve/pii/S0167947320300517ae974a485f413a2113503eed53cd6c53
10.1016/j.csda.2020.106960