How to Marry Robustness and Applied Statistics

A striking feature of most applied statistical analyses is the use of methods that are well known to be sensitive to outliers or to other departures from the postulated model. Since data contamination is often the rule, rather than the exception, we investigate the reasons for this contradictory (and perhaps unintended) choice. We also provide empirical evidence, in a real-world regression problem concerning international trade, of the advantages of a new approach to data analysis based on monitoring. Our approach enhances the applicability of robust techniques and the interpretation of their results, thus yielding a positive step towards a reconciliation between robustness and applied statistics.

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