| Summary: | The presence of outliers in a dataset can cause the outcome of classical statistical tools to be inaccurate. Especially in a multivariate context, where researchers have to deal with either or both cellwise and casewise outliers. This study investigated the accuracy of the Multiple Discriminant Rule (MDR) when both cellwise and casewise outliers exist in a proportionate manner. Classical MDR (CMDR) was constructed using the classical sample mean ... and sample covariance (S) while Robust MDR (RMDR..) was constructed using the Hodges-Lehmann estimator ... and Robust Covariance ... The simulation was carried out where cellwise outliers were shifted in location value and casewise outliers
were involved with location, covariance and dual influence. Based on the simulation results, despite the performance of both CMDR and RMDR.. being quite close when dealing with cellwise-location and casewise-location outliers, RMDR.. outperformed CMDR in both cellwise-location and casewise-covariance as well as cellwise-location and casewise-dual conditions. In summary, the use of ... in robustifying MDR was competent even though dealing with outliers percentage beyond its tolerance
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