Detecting influential observations in Watson data

作者：Barros C M^*; Amaral G J A; Nascimento A D C; Cysneiros A H M A

来源：Communications in Statistics - Theory and Methods, 2017, 46(14): 6882-6898.

DOI：10.1080/03610926.2016.1139130

• Cook's distance
• Influential observations
• Kullback-Leibler distance
• Outliers
• 62H11

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.

• 出版日期2017