Improving bias in kernel density estimation
Kairat Mynbaev (),
Christopher Withers and
MPRA Paper from University Library of Munich, Germany
For order $q$ kernel density estimators we show that the constant $b_q$ in $bias=b_qh^q+o(h^q)$ can be made arbitrarily small, while keeping the variance bounded. A data-based selection of bq is presented and Monte Carlo simulations illustrate the advantages of the method.
Keywords: Density estimation; bias; higher order kernel (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2014, Revised 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
https://mpra.ub.uni-muenchen.de/75846/1/MPRA_paper_75846.pdf original version (application/pdf)
Journal Article: Improving bias in kernel density estimation (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:75846
Access Statistics for this paper
More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().