Strongly Consistent of Kullback-Leibler Divergence Estimator and Tests for Model Selection Based on a Bias Reduced Kernel Density Estimator

2018/05/18 by Papa Ngom, Ngom, Papa, Freedath Djibril Moussa +3
Mathematics · #62F10 #62F12 #62G07 #62G10 #62G20 #FOS: Computer and information sciences #Methodology (stat.ME) #msc:62F10 #msc:62F12 #msc:62G07 #msc:62G10 #msc:62G20 #stat.ME

paper · pdf · doi:10.48550/arxiv.1805.07088

27 pages, 5 figures

arxiv created 2018/05/18 · arxiv updated 2018/05/21

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.

Related

• Discriminating between two models based on Bregman divergence in small samples
• Distributions of the Kullback–Leibler divergence with applications
• Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence
• Parameter estimation based on cumulative Kullback-Leibler divergence
• Statistical estimation of the Kullback-Leibler divergence