On multiplicative bias correction in kernel density estimation
Abstract
Hjort and Glad (1995) present a method for semiparametric density estimation. Relative to the ordinary kernel density estimator, this technique performs much better when a parametric vehicle distribution fits the data, and otherwise performs at broadly the same level. Jones, Linton, and Nielsen (1995) present a somewhat similar method for density estimation which has higher order bias for all sufficiently smooth densities. In this paper, we combine the two methods. We show that, theoretically, the desired properties of general higher order bias allied with even better performance for an appropriate vehicle model are achieved. Simulations suggest that the new estimator realises only a little of its theoretical potential in practice for small to moderately large sample sizes.
Comments: 9 pages, no figures. This is the authors' manuscript, Statistical Research Report, Department of Mathematics, University of Oslo, later published, in essentially similar form, in Sankyha: the Indian Journal of Statistics, Series A, 2009, pages 422.430
Cite
@article{arxiv.2605.30071,
title = {On multiplicative bias correction in kernel density estimation},
author = {M. C. Jones and D. F. Signorini and Nils Lid Hjort},
journal= {arXiv preprint arXiv:2605.30071},
year = {2026}
}