The non-Parametric models difference from non-parametric statistic . It does not depend on determination or prior-hypothesis in building the model structure ,but it depends directly and basically on data .The nature and the number of the parameters is flexible and not fixed. Therefore ,these models are called free distribution models. The methods of non-parametric inference are mathematical processes in order to test statistical hypothesis which do not impose the existence of assumption about frequent distributions of variables, so they are less strength than parametric tests, but they are more robust in case of trespass the basic assumption or the way in which they can not be verified . In this study we estimate the non-parametric regression function directly without depening on any certain parameters by using Nadaraya-Watson methods by using the simulation
Abd-Alhusein Mohammed,M . (2011). Using Nadaraya-Watson kernl Estimator To Estimate Nonparametric Regression function . AL-Qadisiyah Journal For Administrative and Economic sciences, 12(1), 241-250.
MLA
Abd-Alhusein Mohammed,M . "Using Nadaraya-Watson kernl Estimator To Estimate Nonparametric Regression function ", AL-Qadisiyah Journal For Administrative and Economic sciences, 12, 1, 2011, 241-250.
HARVARD
Abd-Alhusein Mohammed M. (2011). 'Using Nadaraya-Watson kernl Estimator To Estimate Nonparametric Regression function ', AL-Qadisiyah Journal For Administrative and Economic sciences, 12(1), pp. 241-250.
CHICAGO
M Abd-Alhusein Mohammed, "Using Nadaraya-Watson kernl Estimator To Estimate Nonparametric Regression function ," AL-Qadisiyah Journal For Administrative and Economic sciences, 12 1 (2011): 241-250,
VANCOUVER
Abd-Alhusein Mohammed M. Using Nadaraya-Watson kernl Estimator To Estimate Nonparametric Regression function . AL-Qadisiyah Journal For Administrative and Economic sciences. 2011;12(1):241-250.
