On kernel density and mode estimates for associated and censored data

Yacine Ferrani, Elias Ould Saïd and Abdelkader Tatachak

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 7, 1853-1862

Abstract: Let {Ti, i ⩾ 1} be a strictly stationary sequence of associated random variables distributed as T. This paper aims to establish strong uniform consistency over a compact set with a rate of a kernel estimator of the underlying density function f when the random variable of interest T is right-censored by another variable C. As a consequence, the almost sure convergence of a new smooth kernel mode estimator θ^n$\hat{\theta }_n$ of the true mode θ of f with rate is stated.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2013.867996 (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:lstaxx:v:45:y:2016:i:7:p:1853-1862

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2013.867996

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().