Arashi, MohammadAsar, YasinYuzbasi, Bahadir2024-08-042024-08-0420210094-96551563-5163https://doi.org/10.1080/00949655.2021.1924174https://hdl.handle.net/11616/99944We 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 estimatorseninfo:eu-repo/semantics/closedAccessBiasing parameterL-1-penaltyLASSOLiu estimationMulticollinearityvariable selectionArticle91153170318310.1080/00949655.2021.19241742-s2.0-85106308953Q2WOS:000649187700001Q3