mlsanityData to 14 Oct 2026

Consistent Kernel Density Estimation with Non-Vanishing Bandwidth

arXiv 1705.08921 ↗First posted 24 May 2017stat.ML cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

Consistency of the kernel density estimator requires that the kernel bandwidth tends to zero as the sample size grows. In this paper we investigate the question of whether consistency is possible when the bandwidth is fixed, if we consider a more general class of weighted KDEs. To answer this question in the affirmative, we introduce the fixed-bandwidth KDE (fbKDE), obtained by solving a quadratic program, and prove that it consistently estimates any continuous square-integrable density. We also establish rates of convergence for the fbKDE with radial kernels and the box kernel under appropriate smoothness assumptions. Furthermore, in an experimental study we demonstrate that the fbKDE compares favorably to the standard KDE and the previously proposed variable bandwidth KDE.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 24 May 2017.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2022: 1.

1 paper.