Detecting influential observations in Watson data

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.

KEYWORDS:

• Cook’s distance
• Influential observations
• Kullback–Leibler distance
• Outliers

MATHEMATICS SUBJECT CLASSIFICATION:

• 62H11

Acknowledgments

We thank the anonymous referees for constructive criticism. The authors are grateful to the financial support of the CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico (Brasil) and FACEPE.