Asymptotic-based bandwidth selection for the presmoothed density estimator with censored data

Maria Jácome and Ricardo Cao

Journal of Nonparametric Statistics, 2008, vol. 20, issue 6, 483-506

Abstract: This paper is concerned with the problem of selecting a suitable bandwidth for the presmoothed density estimator from right-censored data. An asymptotic expression for the mean integrated squared error (MISE) of this estimator is given, and the smoothing parameters minimising it are proved to be consistent approximations of the MISE bandwidths. As a consequence, a bandwidth selector based on plug-in ideas is introduced. We also present a bootstrap bandwidth selector. The performance of both methods is investigated in a simulation study, in which the Kaplan–Meier kernel density estimator has been taken as a relevant competitor.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10485250802280226 (text/html)
Access to full text is restricted to subscribers.

Related works:
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: https://EconPapers.repec.org/RePEc:taf:gnstxx:v:20:y:2008:i:6:p:483-506

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

DOI: 10.1080/10485250802280226

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().