Bivariate elliptical regression for modeling interval-valued data

This paper introduces a special case of a multivariate regression model with restriction for interval-valued data in the symbolic data analysis framework. This model is less sensitive in the presence of interval outliers since it considers light-heavy tails distributions. Intervals are obtained from classic data according to a fusion process and each interval can be represented by its center and range data or lower and upper bound values. The correlation between the center and range variables or lower and upper bound variables is a fundamental component for constructing the model. Therefore, a study that provides a suitable choice of the representation for intervals in bivariate models is proposed. Simulation studies in the Monte Carlo framework regarding different scenarios of interval data set with and without outliers are carried out to validate the proposed model. An application with real-life interval medical dataset is also performed.