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SLASSO: a scaled LASSO for multicollinear situations

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dc.contributor.author Arashi, M.
dc.contributor.author Asar, Y.
dc.contributor.author Yüzbaşı, B.
dc.date.accessioned 2022-10-06T12:50:29Z
dc.date.available 2022-10-06T12:50:29Z
dc.date.issued 2021
dc.identifier.issn 00949655 (ISSN)
dc.identifier.uri http://hdl.handle.net/11616/71822
dc.description.abstract We propose a re-scaled LASSO by pre-multiplying the LASSO with a matrix term, namely, scaled LASSO (SLASSO), for multicollinear situations. Our numerical study has shown that the SLASSO is comparable with other sparse modeling techniques and often outperforms the LASSO and elastic net. Our findings open new visions about using the LASSO still for sparse modeling and variable selection. We conclude our study by pointing that the same efficient algorithm can solve the SLASSO for solving the LASSO and suggest following the same construction technique for other penalized estimators. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
dc.source Journal of Statistical Computation and Simulation
dc.title SLASSO: a scaled LASSO for multicollinear situations


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