| Abstrakt: |
Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing artificial TABU algorithm to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as sport, chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett's method, and Durbin's method), The non-parametric model is estimated by using kernel smoothing (Nadaraya Watson), K-Nearest Neighbor smoothing and Median smoothing. The TABU algorithms were employed and structured estimating the semi-parametric regression function with measurement errors in the explanatory and dependent variables, then compare the models to choose the best mode, where the comparison between the models is done using the mean square error (MSE). A simulation had been used to study the empirical behavior for the semi-parametric models, with different sample sizes and variances. The most important conclusions that we reached when using statistical methods in estimating parameters and choosing the best model, we found that the Median-Durbin model is the best as it has less MSE, but when using Tabu algorithm showed that the Median-Wald model is the best because it has the lowest MSE. [ABSTRACT FROM AUTHOR] |
