Feb 10, 2017 � We propose a novel estimator based on local likelihood density estimators, that mitigates the boundary biases.

Feb 10, 2017 � Abstract. Estimating expected polynomials of density functions from samples is a basic problem with numerous applications in statistics and�...

This work proposes a novel estimator based on local likelihood density estimators, that mitigates the boundary biases and provides a simple debiasing scheme�...

Estimating expected polynomials of density functions from samples is a basic problem with numerous applications in statistics and information theory.

Let Rk(x) denotes the distance from x to its k-th nearest neighbor point. For a given point x, the kNN density estimator estimates the density by bpknn(x) = k n.

Apr 8, 2019 � The KNN model is defined by allowing a sphere to expand around a given point x until it contains exactly K other points.

Fingerprint. Dive into the research topics of 'Density functional estimators with k-nearest neighbor bandwidths'. Together they form a unique fingerprint.

Introduction � nearest neighbours adaptive... � Finding the optimal bandwidth

The kernel method [1, 12] estimates the density by calculating the convolution of the empirical distribution with a symmetric and normalized kernel function.

We prove finite sample bounds for k-nearest neighbor (k-NN) density estimation, and subsequently apply these bounds to the related problem of mode�...