SLASSO: a scaled LASSO for multicollinear situations

Arashi M., ASAR Y., YÜZBAŞI B.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, cilt.91, sa.15, ss.3170-3183, 2021 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 91 Sayı: 15
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1080/00949655.2021.1924174
  • Dergi Adı: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Communication Abstracts, Metadex, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.3170-3183
  • Anahtar Kelimeler: Biasing parameter, L-1-penalty, LASSO, Liu estimation, Multicollinearity, variable selection, BIASED-ESTIMATION, REGRESSION, REGULARIZATION, ESTIMATORS, SELECTION, RIDGE
  • İnönü Üniversitesi Adresli: Evet

Özet

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