Lasso Estimation of an Interval-Valued Multiple Regression Model
Statistics Theory
2016-02-09 v1 Statistics Theory
Abstract
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.
Cite
@article{arxiv.1602.02408,
title = {Lasso Estimation of an Interval-Valued Multiple Regression Model},
author = {Marta García Bárzana and Ana Colubi and Erricos John Kontoghiorghes},
journal= {arXiv preprint arXiv:1602.02408},
year = {2016}
}