Double shrunken selection operator
Küçük Resim Yok
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Inc
Erişim Hakkı
info:eu-repo/semantics/openAccess
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.
Açıklama
Anahtar Kelimeler
Double shrinking, LASSO, Linear regression model, MSE, Prediction error, Stein-type shrinkage estimator
Kaynak
Communications in Statistics-Simulation and Computation
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
48
Sayı
3
