Open AccessShrinkage estimators for semiparametric regression model
Author(s) -
Hadi Salman Mohammed,
Zakariya Yahya Algamal
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1897/1/012012
Subject(s) - multicollinearity , semiparametric regression , estimator , semiparametric model , regression analysis , statistics , shrinkage , econometrics , linear regression , context (archaeology) , nonparametric regression , regression , mathematics , mean squared error , regression diagnostic , shrinkage estimator , bias of an estimator , polynomial regression , minimum variance unbiased estimator , geography , archaeology
Semiparametric regression models are extensions of linear regression models to include a nonparametric function of some explanatory variables. In semiparametric regression model researchers often encounter the problem of multicollinearity. In the context of ridge estimator, the estimation of shrinkage parameter plays an important role in analyzing data. In this paper, numerous selection methods of the shrinkage parameter of ridge estimator are explored and investigated. Our Monte Carlo simulation results suggest that some estimators can bring significant improvement relative to others, in terms of mean squared error.