BACON: blocked adaptive computationally efficient outlier nominators. Although it is customary to assume that data are homogeneous, in fact, they often contain outliers or subgroups. Methods for identifying multiple outliers and subgroups must deal with the challenge of establishing a metric that is not itself contaminated by inhomogeneities by which to measure how extraordinary a data point is. For samples of a sufficient size to support sophisticated methods, the computation cost often makes outlier detection unattractive. All multiple outlier detection methods have suffered in the past from a computational cost that escalated rapidly with the sample size. We propose a new general approach, based on the methods of Hadi (1992a,1994) and Hadi and Simonoff (1993) that can be computed quickly — often requiring less than five evaluations of the model being fit to the data, regardless of the sample size. Two cases of this approach are presented in this paper (algorithms for the detection of outliers in multivariate and regression data). The algorithms, however, can be applied more broadly than to these two cases. We show that the proposed methods match the performance of more computationally expensive methods on standard test problems and demonstrate their superior performance on large simulated challenges.

References in zbMATH (referenced in 34 articles , 1 standard article )

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  1. Jiang, Yunlu; Wang, Yan; Zhang, Jiantao; Xie, Baojian; Liao, Jibiao; Liao, Wenhui: Outlier detection and robust variable selection via the penalized weighted LAD-LASSO method (2021)
  2. Kandanaarachchi, Sevvandi; Hyndman, Rob J.: Dimension reduction for outlier detection using DOBIN (2021)
  3. Mehmet Hakan Satman; Shreesh Adiga; Guillermo Angeris; Emre Akadal: LinRegOutliers: A Julia package for detecting outliers in linear regression (2021) not zbMATH
  4. Bagdonavičius, Vilijandas; Petkevičius, Linas: A new multiple outliers identification method in linear regression (2020)
  5. Kandanaarachchi, Sevvandi; Muñoz, Mario A.; Hyndman, Rob J.; Smith-Miles, Kate: On normalization and algorithm selection for unsupervised outlier detection (2020)
  6. Templ, M.; Gussenbauer, J.; Filzmoser, P.: Evaluation of robust outlier detection methods for zero-inflated complex data (2020)
  7. Uzabaci, Ender; Ercan, Ilker; Alpu, Ozlem: Evaluation of outlier detection method performance in symmetric multivariate distributions (2020)
  8. Wada, Kazumi: Outliers in official statistics (2020)
  9. Unwin, Antony: Multivariate outliers and the O3 plot (2019)
  10. Zhao, Junlong; Liu, Chao; Niu, Lu; Leng, Chenlei: Multiple influential point detection in high dimensional regression spaces (2019)
  11. Alrawashdeh, Mufda Jameel; Radwan, Taha Radwan; Abunawas, Kalid Abunawas: Performance of linear discriminant analysis using different robust methods (2018)
  12. Kerkri, Abdelmounaim; Allal, Jelloul; Zarrouk, Zoubir: Robust nonlinear partial least squares regression using the BACON algorithm (2018)
  13. Zhu, Zhe; Welsch, Roy E.: Robust dependence modeling for high-dimensional covariance matrices with financial applications (2018)
  14. De Bin, Riccardo; Boulesteix, Anne-Laure; Sauerbrei, Willi: Detection of influential points as a byproduct of resampling-based variable selection procedures (2017)
  15. Nurunnabi, A. A. M.; Nasser, M.; Imon, A. H. M. R.: Identification and classification of multiple outliers, high leverage points and influential observations in linear regression (2016)
  16. Alih, Ekele; Ong, Hong Choon: An outlier-resistant test for heteroscedasticity in linear models (2015)
  17. Alih, Ekele; Ong, Hong Choon: Cluster-based multivariate outlier identification and re-weighted regression in linear models (2015)
  18. Hubert, Mia; Rousseeuw, Peter; Vanpaemel, Dina; Verdonck, Tim: The DetS and DetMM estimators for multivariate location and scatter (2015)
  19. Du, Bo; Zhang, Liangpei: Target detection based on a dynamic subspace (2014) ioport
  20. Hubert, M.; Rousseeuw, P.; Vakili, K.: Shape bias of robust covariance estimators: an empirical study (2014)

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