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A New Graphical Method for Detecting Single and Multiple Outliers in Univariate and Multivariate Data

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  • J. Bacon‐Shone
  • W. K. Fung

Abstract

A new graphical approach based on Wilks's (1963) statistic is proposed. The method is found to be useful in the detection of outliers in univariate and multivariate data. Masking and swamping effects in the sample are easily revealed. The method is illustrated with examples and simulations.

Suggested Citation

  • J. Bacon‐Shone & W. K. Fung, 1987. "A New Graphical Method for Detecting Single and Multiple Outliers in Univariate and Multivariate Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(2), pages 153-162, June.
    • Handle: RePEc:bla:jorssc:v:36:y:1987:i:2:p:153-162
    DOI: 10.2307/2347547
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    Cited by:

    1. Schwertman, Neil C. & Owens, Margaret Ann & Adnan, Robiah, 2004. "A simple more general boxplot method for identifying outliers," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 165-174, August.
    2. Schwertman, Neil C. & de Silva, Rapti, 2007. "Identifying outliers with sequential fences," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3800-3810, May.
    3. Becker, Claudia & Gather, Ursula, 1997. "The masking breakdown point of multivariate outlier identification rules," Technical Reports 1997,09, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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