Nonparametric Estimation of Renyi Divergence and Friends
Machine Learning
2014-05-13 v2 Statistics Theory
Statistics Theory
Abstract
We consider nonparametric estimation of , Renyi- and Tsallis- divergences between continuous distributions. Our approach is to construct estimators for particular integral functionals of two densities and translate them into divergence estimators. For the integral functionals, our estimators are based on corrections of a preliminary plug-in estimator. We show that these estimators achieve the parametric convergence rate of when the densities' smoothness, , are both at least where is the dimension. We also derive minimax lower bounds for this problem which confirm that is necessary to achieve the rate of convergence. We validate our theoretical guarantees with a number of simulations.
Keywords
Cite
@article{arxiv.1402.2966,
title = {Nonparametric Estimation of Renyi Divergence and Friends},
author = {Akshay Krishnamurthy and Kirthevasan Kandasamy and Barnabas Poczos and Larry Wasserman},
journal= {arXiv preprint arXiv:1402.2966},
year = {2014}
}