فهرست نشریات علمی دسترسی آزاد ایران

When the parametric model does not hold, and we cannot fit a parametric model to the data, the true density may be estimated non-parametrically, as in the case of a kernel estimate. The purpose of this paper is to present a comparison between parametric and non-parametric models. The parametric investigation contains Vuong's test, and tracking interval based on the known maximum likelihood estimation theory. The presented non-parametric analysis involves kernel density estimation. Modified differences of Kullback-Leibler criteria between two rival models and Vuong's test, have been considered. In this circumstance, we address the problem of cross-validation estimation of variance for Kullback-Leibler divergence between the true but unknown density and its kernel estimator. A simulation study and data analysis have shown that the parametric density is a more realistic estimate of the data generating density.