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arXiv · 1801.00852

A Concentration Result of Estimating Phi-Divergence using Data Dependent Partition

Fengqiao Luo·Sanjay Mehrotra
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Abstract

Estimation of the $\phi$-divergence between two unknown probability distributions using empirical data is a fundamental problem in information theory and statistical learning. We consider a multi-variate generalization of the data dependent partitioning method for estimating divergence between the two unknown distributions. Under the assumption that the distribution satisfies a power law of decay, we provide a convergence rate result for this method on the number of samples and hyper-rectangles required to ensure the estimation error is bounded by a given level with a given probability.

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Fengqiao Luo, Sanjay Mehrotra. 2018-01-02. A Concentration Result of Estimating Phi-Divergence using Data Dependent Partition. https://arxiv.org/abs/1801.00852

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