Economics at your fingertips  

Improving bias in kernel density estimation

Kairat Mynbaev (), Saralees Nadarajah, Christopher S. Withers and Aziza S. Aipenova

Statistics & Probability Letters, 2014, vol. 94, issue C, 106-112

Abstract: For order q kernel density estimators we show that the constant bq in bias=bqhq+o(hq) 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)
Date: 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)
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Improving bias in kernel density estimation (2014) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2014.07.014

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Haili He ().

Page updated 2020-10-23
Handle: RePEc:eee:stapro:v:94:y:2014:i:c:p:106-112