A comparison of the Semiparametric Estimators model smoothing methods different using
DOI:
https://doi.org/10.33095/jeas.v20i75.595Keywords:
: partial linear regression model, cubic smoothing spline, kernel smoothing Nadaraya-Watson estimator.Abstract
In this paper, we made comparison among different parametric ,nonparametric and semiparametric estimators for partial linear regression model users parametric represented by ols and nonparametric methods represented by cubic smoothing spline estimator and Nadaraya-Watson estimator, we study three nonparametric regression models and samples sizes n=40,60,100,variances used σ2=0.5,1,1.5 the results for the first model show that N.W estimator for partial linear regression model(PLM) is the best followed the cubic smoothing spline estimator for (PLM),and the results of the second and the third model show that the best estimator is C.S.S.followed by N.W estimator for (PLM) ,the results also indicated that the lowest estimator and representation of the models used is the parametric estimator OLS followed by nanparametric estimator N.W.
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