Feasible robust Liu estimator to combat outliers and multicollinearity effects in restricted semiparametric regression model

Regression analysis frequently encounters two issues: multicollinearity among the explanatory variables, and the existence of outliers in the data set. Multicollinearity in the semiparametric regression model causes the variance of the ordinary least-squares estimator to become inflated. Furthermore, the existence of multicollinearity may lead to wide confidence intervals for the individual parameters and even produce estimates with wrong signs. On the other hand, as is often known, the ordinary least-squares estimator is extremely sensitive to outliers, and it may be completely corrupted by the existence of even a single outlier in the data. Due to such drawbacks of the least-squares method, a robust Liu estimator based on the least trimmed squares (LTS) method for the regression parameters is introduced under some linear restrictions on the whole parameter space of the linear part in a semiparametric model. Considering that the covariance matrix of the error terms is usually unknown in practice, the feasible forms of the proposed estimators are substituted, and their asymptotic distributional properties are derived. Moreover, necessary and sufficient conditions for the superiority of the Liu type estimators over their counterparts for choosing the biasing Liu parameter d are extracted. The performance of the feasible type of robust Liu estimators is compared with the classical ones in constrained semiparametric regression models using extensive Monte-Carlo simulation experiments and a real data example.