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.
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Bárzana, M.G., Colubi, A., Kontoghiorghes, E.J. (2015). Lasso Estimation of an Interval-Valued Multiple Regression Model. In: Grzegorzewski, P., Gagolewski, M., Hryniewicz, O., Gil, M. (eds) Strengthening Links Between Data Analysis and Soft Computing. Advances in Intelligent Systems and Computing, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-319-10765-3_22
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DOI: https://doi.org/10.1007/978-3-319-10765-3_22
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10764-6
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