Improved penalty strategies in linear regression models
Küçük Resim Yok
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
National Statistical Institute
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
We suggest pretest and shrinkage ridge estimation strategies for linear regression models. We investigate the asymptotic properties of suggested estimators. Further, a Monte Carlo simulation study is conducted to assess the relative performance of the listed estimators. Also, we numerically compare their performance with Lasso, adaptive Lasso and SCAD strategies. Finally, a real data example is presented to illustrate the usefulness of the suggested methods. © 2017, National Statistical Institute. All rights reserved.
Açıklama
Anahtar Kelimeler
Asymp-totic and Simulation, Full Model, Multicollinearity, Pretest and Shrinkage Estimation, Sub-model
Kaynak
REVSTAT-Statistical Journal
WoS Q Değeri
Scopus Q Değeri
Q3
Cilt
15
Sayı
2
