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Functional outlier detection and taxonomy by sequential transformations
Computational Statistics & Data Analysis ( IF 1.8 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.csda.2020.106960
Wenlin Dai , Tomáš Mrkvička , Ying Sun , Marc G. Genton

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.

中文翻译:

通过顺序转换进行功能异常值检测和分类

异常观察可能会严重损害功能数据分析,异常观察可以根据其偏离大量数据的方式分为幅度或形状异常值。识别幅度异常值相对容易,而检测形状异常值则更具挑战性。我们建议通过数据转换将形状异常值转换为幅度异常值,并使用功能箱线图检测它们。除了简化检测程序外,依次应用多个转换为标记的异常值提供了合理的分类法。这里还定义了一个联合功能排序,它由几个转换组成。进行模拟研究以使用不同的功能深度概念评估所提出方法的性能。
更新日期:2020-09-01
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