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arXiv · 1805.07088

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

Papa Ngom·Freedath Djibril Moussa·Jean de Dieu Nkurunziza
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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.

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Papa Ngom, Freedath Djibril Moussa, Jean de Dieu Nkurunziza. 2018-05-18. Strongly Consistent of Kullback-Leibler Divergence Estimator and Tests for Model Selection Based on a Bias Reduced Kernel Density Estimator. https://arxiv.org/abs/1805.07088

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