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Detecting influential observations in Watson data

Author

Listed:
  • C. M. Barros
  • G. J. A. Amaral
  • A. D. C. Nascimento
  • A. H. M. A. Cysneiros

Abstract

A method for detecting outliers in axial data has been proposed by Best and Fisher (1986). For extending that work, we propose four new methods. Two of them are suitable for outlier detection and they depend on the classic geodesic distance and a modified version of this distance. The other two procedures, which are designed for influential observation detection, are based on the Kullback–Leibler and Cook’s distances. Some simulation experiments are performed to compare all considered methods. Detection and error rates are used as comparison criteria. Numerical results provide evidence in favor of the KL distance.

Suggested Citation

  • C. M. Barros & G. J. A. Amaral & A. D. C. Nascimento & A. H. M. A. Cysneiros, 2017. "Detecting influential observations in Watson data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(14), pages 6882-6898, July.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:14:p:6882-6898
    DOI: 10.1080/03610926.2016.1139130
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    Cited by:

    1. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.

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