City Research Online

Bandwidth selection for kernel density estimation with length-biased data

Gonzalez-Manteiga, W, Borrajo, MI & Martinez-Miranda, M. D. (2017). Bandwidth selection for kernel density estimation with length-biased data. Journal of Nonparametric Statistics, 29(3), pp. 636-668. doi: 10.1080/10485252.2017.1339309

Abstract

Length-biased data are a particular case of weighted data, which arise in many situations: biomedicine, quality control or epidemiology among others. In this paper we study the theoretical properties of kernel density estimation in the context of length-biased data, proposing two consistent bootstrap methods that we use for bandwidth selection. Apart from the bootstrap bandwidth selectors we suggest a rule-of-thumb. These bandwidth selection proposals are compared with a least-squares cross-validation method. A simulation study is accomplished to understand the behaviour of the procedures in finite samples.

Publication Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of Nonparametric Statistics on 23/06/2017, available online: http://dx.doi.org/10.1080/10485252.2017.1339309.
Publisher Keywords: Bootstrap, Rule-of-thumb, Cross-validation, Non-parametric, Weighted data
Subjects: H Social Sciences > HA Statistics
Departments: Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:
[thumbnail of NPSJ_paper2017.pdf]
Preview
Text - Accepted Version
Download (605kB) | Preview

Export

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login