Estimation in partially linear models
Computational Statistics & Data Analysis
Ridge estimation of a semiparametric regression model
Journal of Computational and Applied Mathematics
Linear Models and Generalizations: Least Squares and Alternatives
Linear Models and Generalizations: Least Squares and Alternatives
Estimation in partially linear models and numerical comparisons
Computational Statistics & Data Analysis
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In this paper we consider the semiparametric regression model, y=X@b+f+@e. Recently, Hu [11] proposed ridge regression estimator in a semiparametric regression model. We introduce a Liu-type (combined ridge-Stein) estimator (LTE) in a semiparametric regression model. Firstly, Liu-type estimators of both @b and f are attained without a restrained design matrix. Secondly, the LTE estimator of @b is compared with the two-step estimator in terms of the mean square error. We describe the almost unbiased Liu-type estimator in semiparametric regression models. The almost unbiased Liu-type estimator is compared with the Liu-type estimator in terms of the mean squared error matrix. A numerical example is provided to show the performance of the estimators.