Pendekatan Estimator Kernel Untuk Estimasi Densitas Mulus

Let X i : i  1,2,., nbe independent observation data from a distribution with density function f. There are two basic approaches for estimating f, the parametric and the nonparametric approaches. In nonparametric approaches, an unknown density function f. The function f is assumed to be a smooth function, so the function f could be estimated by kernel estimator. The smoothing level of kernel estimator depends to the smoothing parameter. The big smoothing parameter gives the estimation function which over smooth and the contrary.