Nonparametric depth-based multivariate outlier identifiers, and masking robustness properties
نویسندگان
چکیده
منابع مشابه
Nonparametric Depth-Based Multivariate Outlier Identifiers, and Masking Robustness Properties
In extending univariate outlier detection methods to higher dimension, various issues arise: limited visualization methods, inadequacy of marginal methods, lack of a natural order, limited parametric modeling, and, when using Mahalanobis distance, restriction to ellipsoidal contours. To address and overcome such limitations, we introduce nonparametric multivariate outlier identifiers based on m...
متن کاملNonparametric Depth-Based Multivariate Outlier Identifiers, and Robustness Properties
In extending univariate outlier detection methods to higher dimension, various special issues arise, such as limitations of visualization methods, inadequacy of marginal methods, lack of a natural order, limited scope of parametric modeling, and restriction to ellipsoidal contours when using Mahalanobis distance methods. Here we pass beyond these limitations via an approach based on depth funct...
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In the wide-ranging scope of modern statistical data analysis, a key task is identification of outliers. In using an outlier identification procedure, one needs to know its robustness against masking (an “outlier” is undetected) and swamping (a “nonoutlier” is classified as an “outlier”), possibilities which can come about due to the presence of outliers. Study of these issues together is neces...
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In this article, first, we introduce depth function as a function for center-outward ranking. Then we present and use half space or Tukey depth function as one of the most popular depth functions. In the following, multivariate nonparametric tests for location and scale difference between two population are expressed by ranking and statistics based on depth versus depth plot. Finally, accord...
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ژورنال
عنوان ژورنال: Journal of Statistical Planning and Inference
سال: 2010
ISSN: 0378-3758
DOI: 10.1016/j.jspi.2009.07.004