Lasso estimation of an interval-valued multiple regression model. (English) Zbl 1337.62168

Grzegorzewski, Przemysław (ed.) et al., Strengthening links between data analysis and soft computing. Collected papers based on the presentations at the 7th international conference on soft methods in probability and statistics, SMPS 2014, Warsaw, Poland, September 22–24, 2014. Cham: Springer (ISBN 978-3-319-10764-6/pbk; 978-3-319-10765-3/ebook). Advances in Intelligent Systems and Computing 315, 185-191 (2015).
Summary: 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.
For the entire collection see [Zbl 1312.68007].


62J07 Ridge regression; shrinkage estimators (Lasso)
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