Shrinkage estimators for semiparametric regression model

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 shrinka...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-05, Vol.1897 (1), p.12012
Main Authors: Mohammed, Hadi Salman, Algamal, Zakariya Yahya
Format: Article
Language:English
Subjects:
Estimators
Monte Carlo simulation
Parameter estimation
Physics
Regression models
Shrinkage
Citations: Items that this one cites
Online Access:Get full text
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1897/1/012012