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Features and performance of some outlier detection methods

Author

Listed:
  • G. Barbato
  • E. M. Barini
  • G. Genta
  • R. Levi

Abstract

A review of several statistical methods that are currently in use for outlier identification is presented, and their performances are compared theoretically for typical statistical distributions of experimental data, considering values derived from the distribution of extreme order statistics as reference terms. A simple modification of a popular, broadly used method based upon box-plot is introduced, in order to overcome a major limitation concerning sample size. Examples are presented concerning exploitation of methods considered on two data sets: a historical one concerning evaluation of an astronomical constant performed by a number of leading observatories and a substantial database pertaining to an ongoing investigation on absolute measurement of gravity acceleration, exhibiting peculiar aspects concerning outliers. Some problems related to outlier treatment are examined, and the requirement of both statistical analysis and expert opinion for proper outlier management is underlined.

Suggested Citation

  • G. Barbato & E. M. Barini & G. Genta & R. Levi, 2011. "Features and performance of some outlier detection methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2133-2149.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2133-2149
    DOI: 10.1080/02664763.2010.545119
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    Cited by:

    1. Elyasiani, Elyas & Movaghari, Hadi, 2022. "Determinants of corporate cash holdings: An application of a robust variable selection technique," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 967-993.
    2. Kar, Manaswinee & Sadhukhan, Shubhajit & Parida, Manoranjan, 2022. "Assessing commuters’ perceptions towards improvement of intermediate public transport as access modes to metro stations," Transport Policy, Elsevier, vol. 129(C), pages 140-155.
    3. Eleonora Cutrini & Federico Ninivaggi, 2024. "Pandemic crisis and firm survival: evidence from the Italian manufacturing industry," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 51(2), pages 265-297, June.
    4. Ciniro A. L. Nametala & Jonas Villela de Souza & Alexandre Pimenta & Eduardo Gontijo Carrano, 2023. "Use of Econometric Predictors and Artificial Neural Networks for the Construction of Stock Market Investment Bots," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 743-773, February.
    5. Ayush Jain & Smit Marvaniya & Shantanu Godbole & Vitobha Munigala, 2020. "A Framework for Crop Price Forecasting in Emerging Economies by Analyzing the Quality of Time-series Data," Papers 2009.04171, arXiv.org.

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