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The Study of Outliers: Purpose and Model

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  • Vic Barnett

Abstract

Outliers may influence the analysis of a set of data in various different ways. Some practical examples are used to motivate a categorization of the different aims in handling outliers and of the different models which might be employed to reflect the presence of outliers.

Suggested Citation

  • Vic Barnett, 1978. "The Study of Outliers: Purpose and Model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 27(3), pages 242-250, November.
    • Handle: RePEc:bla:jorssc:v:27:y:1978:i:3:p:242-250
    DOI: 10.2307/2347159
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    Cited by:

    1. Rifo, L.R. & Santos, J.D., 2012. "A note on conflict of information and subexponential densities," Statistics & Probability Letters, Elsevier, vol. 82(4), pages 840-842.
    2. Nirpeksh Kumar, 2013. "A procedure for testing suspected observations," Statistical Papers, Springer, vol. 54(2), pages 471-478, May.
    3. Liu, Ling & Henley, John & Mousavi, Mohammad Mahdi, 2021. "Foreign interfirm networks and internationalization: Evidence from sub-Saharan Africa," Journal of International Management, Elsevier, vol. 27(1).
    4. Hochkamp, Florian & Rabe, Markus, 2022. "Outlier detection in data mining: Exclusion of errors or loss of information?," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 91-117, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    5. Carling, Kenneth, 1998. "Resistant outlier rules and the non-Gaussian case," Working Paper Series 2001:7, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    6. Carling, Kenneth, 2000. "Resistant outlier rules and the non-Gaussian case," Computational Statistics & Data Analysis, Elsevier, vol. 33(3), pages 249-258, May.
    7. Schwertman, Neil C. & de Silva, Rapti, 2007. "Identifying outliers with sequential fences," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3800-3810, May.

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