TY  - JOUR
AV  - none
SN  - 1524-1904,
SP  - 228
N2  - This paper introduces some methods for outlier identification in the regression setting, motivated by the analysis of steelmaking process data. The proposed methodology extends to the regression setting the boxplot rule, commonly used for outlier screening with univariate data. The focus here is on bivariate settings with a single covariate, but extensions are possible. The proposal is based on quantile regression, including an additional transformation parameter for selecting the best scale for linearity of the conditional quantiles. 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 experiments have been carried out to study the swamping and masking properties of the proposal. The methodology is also illustrated by some real life examples, taking as the response variable the energy consumed in the melting process
ID  - eprints3490
JF  - Applied Stochastic Models in Business and Industry
IS  - 2
Y1  - 2015/03//
VL  - 32
TI  - Simple outlier labeling based on quantile regression, with application to the steelmaking process
PB  - Wiley
UR  - http://doi.org/10.1002/asmb.2146
A1  - Bellio, Ruggero
A1  - Coletto, Mauro
EP  - 242
ER  -