Survival function estimation with non parametric adaptive refined descriptive sampling algorithm: A case study

Megdouda Ourbih-Tari and Mahdia Azzal

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 12, 5840-5850

Abstract: This paper proposes non parametric adaptive refined descriptive sampling algorithm (NARDS) to estimate the survival function in lifetime models using Kaplan–Meier and Fleming–Harrington estimators. Simulations were performed using real data to generate inputs. The obtained results prove that NARDS works well on non parametric data.

Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2015.1065328 (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:46:y:2017:i:12:p:5840-5850

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

DOI: 10.1080/03610926.2015.1065328

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 ().