Generalized difference-based weighted mixed almost unbiased liu estimator in semiparametric regression models - AKDENİZ, ESRA (TAYLOR & FRANCIS INC, 2022)
In classical linear regression analysis problems, the ordinary least-squares (OLS) estimation is the popular method to obtain the regression weights, given the essential assumptions are satisfied. However, often, in real-life studies, the response data and its associated explanatory variables do not meet the required conditions, in particular under multicollinearity, and hence results can be misleading. To overcome such problem, this paper introduces a novel generalized difference-based weighted mixed almost unbiased Liu estimator. The performance of this new estimator is evaluated against the classical estimators using the mean squared error. This is followed by an approach to select the Liu parameter and in this context, a non-stochastic weight is also considered. Monte Carlo simulation experiments are executed to assess the performance of the new estimator and subsequently,we illustrate its application to a real-life data example.
Keywords: Differencing matrix; generalized difference-based weighted mixed almost unbiased Liu estimator; multicollinearity; semiparametric regression model; stochastic restriction; BIASED-ESTIMATORS; RIDGE ESTIMATORS; PARAMETERS; EFFICIENCY; ERROR