Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime
Küçük Resim Yok
Tarih
2020
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.
Açıklama
Anahtar Kelimeler
Generalised ridge regression, low-dimensional and high-dimensional data, multicollinearity, penalty estimation, shrinkage estimation
Kaynak
International Statistical Review
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
88
Sayı
1
