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

中文翻译：

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SLASSO：用于多重共线情况的缩放 LASSO

对于多重共线情况，我们通过将 LASSO 与矩阵项预先相乘，即缩放 LASSO (SLASSO) 来提出重新缩放的 LASSO。我们的数值研究表明，SLASSO 可与其他稀疏建模技术相媲美，并且通常优于 LASSO 和弹性网络。我们的发现为使用 LASSO 仍然进行稀疏建模和变量选择开辟了新视野。我们通过指出相同的有效算法可以解决 SLASSO 来解决 LASSO 并建议对其他惩罚估计量采用相同的构造技术来结束我们的研究