Double shrunken selection operator


Yuzbasi B. , Arashi M.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, cilt.48, ss.666-674, 2019 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 48 Konu: 3
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1080/03610918.2017.1395040
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Sayfa Sayıları: ss.666-674

Özet

The least absolute shrinkage and selection operator (LASSO) is a prominent estimator which selects significant (under some sense) features and kills insignificant ones. Indeed the LASSO shrinks features larger than a noise level to zero. In this article, we force LASSO to be shrunken more by proposing a Stein-type shrinkage estimator emanating from the LASSO, namely the Stein-type LASSO. The newly proposed estimator proposes good performance in risk sense numerically. Variants of this estimator have smaller relative MSE and prediction error, compared to the LASSO, in the analysis of prostate cancer dataset.