Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/14733
Title: Lasso estimation of an interval-valued multiple regression model
Authors: Bárzana, Marta García 
Colubi, Ana
Kontoghiorghes, Erricos John
metadata.dc.contributor.other: Κοντογιώργης, Ερρίκος
Major Field of Science: Social Sciences
Field Category: Economics and Business
Keywords: Interval-valued data;Lasso estimation;Multiple regression;Constraint theory
Issue Date: 1-Jan-2015
Source: Advances in Intelligent Systems and Computing Volume 315, 2015, Pages 185-191
Volume: 315
Journal: Advances in Intelligent Systems and Computing
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.
URI: https://hdl.handle.net/20.500.14279/14733
ISSN: 21945357
DOI: 10.1007/978-3-319-10765-3_22
Rights: © Springer International Publishing 2015.
Type: Article
Affiliation : University of Oviedo 
Cyprus University of Technology 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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