  A multiple interval-valued linear regression model considering all the
cross-relationships between the mids and spreads of the intervals has been
introduced recently. A least-squares estimation of the regression parameters
has been carried out by transforming a quadratic optimization problem with
inequality constraints into a linear complementary problem and using Lemke's
algorithm to solve it. Due to the irrelevance of certain cross-relationships,
an alternative estimation process, the LASSO (Least Absolut Shrinkage and
Selection Operator), is developed. A comparative study showing the differences
between the proposed estimators is provided.
