Strongly Consistent of Kullback-Leibler Divergence Estimator and Tests for Model Selection Based on a Bias Reduced Kernel Density Estimator
Methodology
2018-05-21 v1
Abstract
In this paper, we study the strong consistency of a bias reduced kernel density estimator and derive a strongly con- sistent Kullback-Leibler divergence (KLD) estimator. As application, we formulate a goodness-of-fit test and an asymptotically standard normal test for model selection. The Monte Carlo simulation show the effectiveness of the proposed estimation methods and statistical tests.
Keywords
Cite
@article{arxiv.1805.07088,
title = {Strongly Consistent of Kullback-Leibler Divergence Estimator and Tests for Model Selection Based on a Bias Reduced Kernel Density Estimator},
author = {Papa Ngom and Freedath Djibril Moussa and Jean de Dieu Nkurunziza},
journal= {arXiv preprint arXiv:1805.07088},
year = {2018}
}
Comments
27 pages, 5 figures