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arXiv · 0810.5302

A class of R\'{e}nyi information estimators for multidimensional densities

Nikolai Leonenko·Luc Pronzato·Vippal Savani
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Abstract

A class of estimators of the R\'{e}nyi and Tsallis entropies of an unknown distribution $f$ in $\mathbb{R}^m$ is presented. These estimators are based on the $k$th nearest-neighbor distances computed from a sample of $N$ i.i.d. vectors with distribution $f$. We show that entropies of any order $q$, including Shannon's entropy, can be estimated consistently with minimal assumptions on $f$. Moreover, we show that it is straightforward to extend the nearest-neighbor method to estimate the statistical distance between two distributions using one i.i.d. sample from each. (Wit Correction.)

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Nikolai Leonenko, Luc Pronzato, Vippal Savani. 2008-10-29. A class of R\'{e}nyi information estimators for multidimensional densities. https://doi.org/10.1214/07-aos539

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