Detection of multiple outliners in linear regression using nonparametric methods

TL;DRAbstract

There have been considerable interest in recent years in the detection and accommodation of multiple outliers in linear regression. However, most of them are complicated and unappealing to users with no mathematical background. The clustering algorithm from Sebert et al. (1998) is discussed and used since it is easy to understand with interesting proposed approach and have a good performance in detecting the presence of outliers. Generally, method proposed by Sebert et al. (1998) is based on the use of single linkage clustering algorithm with the Euclidean distances to cluster the points in the plots of standard predicted versus residuals values from a linear regression model. The predicted and residual values are obtained from an ordinary least squares fit of the data. The algorithm is described and is shown to perform well on classic multiple outlier data sets. A modification is done to the Sebert’s method by replacing the least squares (LS) with two robust estimators. Method 1 is

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