تقدير دالة انحدار الحرف اللبي في الانحدار المتعدد اللامعلمي باستعمال المحاكاة

Abstract EN

In general, researchers and statisticians in particular have been usually used non-parametric regression models when the parametric methods failed to fulfillment their aim to analyze the models precisely.

In this case the parametic methods are useless so they turn to non-parametric methods for its easiness in programming.

Non-parametric methods can also used to assume the parametric regression model for subsequent use.

Moreover, as an advantage of using non-parametric methods is to solve the problem of Multi-Colinearity between explanatory variables combined with nonlinear data.

This problem can be solved by using kernel ridge regression which depend on what so-called bandwidth estimation (smoothing parameters).

Therefore, for this purpose two different methods were used to estimate the smoothing parameter (Maximum Likelihood Cross-Validation (MLCV) and Akaike Information Criterion (AIC)).

Furthermore, a comparision between the previouse methods had been provided using simulation technique , and the method of Akaike Information Criterion (AIC) has been found to be the best for the Gaussian function