Lasso estimation of an interval-valued multiple regression model
File(s)
Journal
Advances in Intelligent Systems and Computing
Date Issued
January 1, 2015
Abstract
© Springer International Publishing Switzerland 2015. 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.