Density Estimation Using the Sinc Kernel
Statistics Theory
2026-05-11 v1 Statistics Theory
Abstract
This paper deals with the kernel density estimator based on the so-called sinc (or Fourier integral) kernel . We study in detail both asymptotic and finite sample properties of this estimator. It is shown that, contrary to widespread opinion, the sinc estimator is superior to other estimators in many respects: it is more accurate for quite moderate values of the sample size, has better asymptotics in non-smooth case (the density to be estimated has only first derivative), is more convenient for the bandwidth selection, etc.
Cite
@article{arxiv.2605.07967,
title = {Density Estimation Using the Sinc Kernel},
author = {Ingrid Kristine Glad and Nils Lid Hjort and Nikolai G. Ushakov},
journal= {arXiv preprint arXiv:2605.07967},
year = {2026}
}
Comments
20 pages, no figures. Preprint, Department of Mathematical Statistics, Norwegian University of Science and Technology, Trondheim, no. 2, 2007; arXiv'd for broader visibility and for direct use in a forthcoming paper