The problem of outliers in statistical data has attracted many researchers for a
long time. Consequently, numerous outlier detection methods have been proposed
in the statistical literature. However, no consensus has emerged as to which
method is uniformly better than the others or which one is recommended for
use in practical situations. In this article, we perform an extensive comparative
Monte Carlo simulation study to assess the performance of the multiple outlier
detection methods that are either recently proposed or frequently cited in the outlier
detection literature. Our simulation experiments include a wide variety of realistic
and challenging regression scenarios. We give recommendations on which method
is superior to others under what conditions.
Keywords Clustering; Influence matrix; Outlier detection; Regression.