Robust regression with application to symbolic interval data

This paper presents a robust regression model that deals with cases that have interval-valued outliers in the input data set. Each interval of the input data is represented by its range and midpoint and the fitting to interval-valued data is not sensible in the presence of midpoint and/or range outliers on the interval response. The predictions of the lower and upper bounds of new intervals are performed and simulation studies are carried out to validate these predictions. Two applications with real-life interval data sets are considered. The prediction quality is assessed by a mean magnitude of relative error calculated from a test data set.

► This study aims to propose a robust regression model for interval data sets. ► These data sets contain interval-valued outliers. ► Each interval is represented by its midpoint and range. ► Experiments with real-life and simulated interval data sets are considered. ► The robustness of this model is shown.