Minimax Optimal Estimation of KL Divergence for Continuous Distributions

Puning Zhao; Lifeng Lai

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arXiv · 2002.11599

Minimax Optimal Estimation of KL Divergence for Continuous Distributions

Puning Zhao·Lifeng Lai

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Abstract

Estimating Kullback-Leibler divergence from identical and independently distributed samples is an important problem in various domains. One simple and effective estimator is based on the k nearest neighbor distances between these samples. In this paper, we analyze the convergence rates of the bias and variance of this estimator. Furthermore, we derive a lower bound of the minimax mean square error and show that kNN method is asymptotically rate optimal.

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Puning Zhao, Lifeng Lai. 2020-02-26. Minimax Optimal Estimation of KL Divergence for Continuous Distributions. https://arxiv.org/abs/2002.11599

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