{"id":168751,"date":"2014-07-01T00:00:00","date_gmt":"2014-07-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/nonparametric-estimation-of-renyi-divergence-and-friends\/"},"modified":"2020-08-28T16:58:31","modified_gmt":"2020-08-28T23:58:31","slug":"nonparametric-estimation-of-renyi-divergence-and-friends","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/nonparametric-estimation-of-renyi-divergence-and-friends\/","title":{"rendered":"Nonparametric Estimation of Renyi Divergence and Friends"},"content":{"rendered":"
\n

We consider nonparametric estimation of L 2, Renyi-\u03b1 and Tsallis-\u03b1 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 n \u22121\/2 when the densities\u2019 smoothness, s, are both at least d\/4 where d is the dimension. We also derive minimax lower bounds for this problem which confirm that s>d\/4 is necessary to achieve the n \u22121\/2 rate of convergence. We validate our theoretical guarantees with a number of simulations.<\/p>\n<\/div>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

We consider nonparametric estimation of L 2, Renyi-\u03b1 and Tsallis-\u03b1 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 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