Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators

Part of Advances in Neural Information Processing Systems 29 (NIPS 2016)

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Shashank Singh, Barnabas Poczos


We provide finite-sample analysis of a general framework for using k-nearest neighbor statistics to estimate functionals of a nonparametric continuous probability density, including entropies and divergences. Rather than plugging a consistent density estimate (which requires k → ∞ as the sample size n → ∞) into the functional of interest, the estimators we consider fix k and perform a bias correction. This can be more efficient computationally, and, as we show, statistically, leading to faster convergence rates. Our framework unifies several previous estimators, for most of which ours are the first finite sample guarantees.