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On Rohlf's Method for the Detection of Outliers in Multivariate Data

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  • Caroni, C.
  • Prescott, P.

Abstract

Rohlf (1975, Biometrics31, 93-101) proposed a method of detecting outliers in multivariate data by testing the largest edge of the minimum spanning tree. It is shown here that tests against the gamma distribution are extremely liberal. Furthermore, results depend on the correlation structure of the data if Euclidean distances are used. While the use of generalized distances might avoid this difficulty, the construction of the robust estimates required to carry out the test with generalized distances provides in itself information on outliers which leaves Rohlf's procedure superfluous. It is concluded that Rohlf's method does not provide a useful formal test.

Suggested Citation

  • Caroni, C. & Prescott, P., 1995. "On Rohlf's Method for the Detection of Outliers in Multivariate Data," Journal of Multivariate Analysis, Elsevier, vol. 52(2), pages 295-307, February.
    • Handle: RePEc:eee:jmvana:v:52:y:1995:i:2:p:295-307
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    Cited by:

    1. Modarres, Reza, 2014. "On the interpoint distances of Bernoulli vectors," Statistics & Probability Letters, Elsevier, vol. 84(C), pages 215-222.
    2. Caroni, C. & Prescott, P., 2002. "Inapplicability of Asymptotic Results on the Minimal Spanning Tree in Statistical Testing," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 487-492, November.
    3. Kirschstein, Thomas & Liebscher, Steffen & Becker, Claudia, 2013. "Robust estimation of location and scatter by pruning the minimum spanning tree," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 173-184.

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