Title : ( SLASSO: a scaled LASSO for multicollinear situations )
Authors: Mohammad Arashi , Y. Asar , B. Yuzbasi ,
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Abstract
We propose a rescaled 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.
Keywords
, Biasing parameter; L1, penalty; LASSO; Liu Estimation; Multicollinearity; Variable selection.
@article{paperid:1084714,
author = {Arashi, Mohammad and Y. Asar and B. Yuzbasi},
title = {SLASSO: a scaled LASSO for multicollinear situations},
journal = {Journal of Statistical Computation and Simulation},
year = {2021},
volume = {91},
number = {15},
month = {October},
issn = {0094-9655},
pages = {3170--3183},
numpages = {13},
keywords = {Biasing parameter; L1-penalty; LASSO; Liu Estimation; Multicollinearity; Variable selection.},
}
%0 Journal Article
%T SLASSO: a scaled LASSO for multicollinear situations
%A Arashi, Mohammad
%A Y. Asar
%A B. Yuzbasi
%J Journal of Statistical Computation and Simulation
%@ 0094-9655
%D 2021
