Nonlinear regression model to symbolic interval-valued variables

This paper introduces a nonlinear regression method to fit a regression model to symbolic interval-valued data set. The nonlinear method will be inspired in the method proposed by and will consider two independent nonlinear regression models fitted over the midpoint and range of the intervals. The assessment of the proposed prediction methods is based on the average behavior of the root mean square error and of the square of the correlation coefficient in the framework of a Monte Carlo experiment. The synthetic data sets taking into account the different degree of nonlinearity between the dependent and the independent interval variables, among others aspects.