This paper describes a procedure for identifying
multiple outliers in
linear regression. This procedure uses a
robust fit which is the least of trimmed of
squares (LTS) and the
single linkage clustering method to obtain the potential outliers. Then multiple-case diagnostics are used to obtain the outliers from these potential outliers. The performance of this procedure is also compared to Serbert''s method. Monte Carlo simulations are used in determining which procedure performed best in all of the linear
regression scenarios. Keywords: Multiple outliers, linear regression, robust fit, Least trimmed of squares, single linkage.
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