(untitled) (SAINTEKBU, 2016)
dth: 0px; "> Given a data set (xi , yi ) and connecting between xi and yi be assumed to follow nonparametric regression model : yi ï€½ m(xi ) ï€«ï¥ i , i ï€½ 1,2,...,n. Regresssion curve of m be assumed is an unknown form and ï¥ i , is an error term in the observations are IID with mean 0 and finite variance ï³ 2. In this paper propose to exist mean conditional estimators with employ the local polinomial method which polinomial degree p = 0 will be formed the Nadaraya-Watson estimator and p = 1 to exist the Locally Linear estimator. Furthemore, with the same method also be existed the comparison both bias and variance. Kernel estimator will be applied of the Canadian Males Data by Murphy and Welch (1990). Key words: Nonparametric estimation, weighted least square, Local polinomial