Simple outlier labeling based on quantileregression, with application to thesteelmaking process

This paper introduces some methods for outlier identiîEUR,cation in the regression setting, motivated by the analysis of steelmakingprocess data. The proposed methodology extends to the regression setting the boxplot rule, commonly used for outlier screening withunivariate data. The focus here is on bivariate settings with a single covariate, but extensions are possible. The proposal is basedon quantile regression, including an additional transformation parameter for selecting the best scale for linearity of the conditionalquantiles. The resulting method is used to perform effective labeling of potential outliers, with a quite low computational complexity,allowing for simple implementation within statistical software as well as commonly used spreadsheets. Some simulation experimentshave been carried out to study the swamping and masking properties of the proposal. The methodology is also illustrated by somereal life examples, taking as the response variable the energy consumed in the melting process.