On estimating the distribution function and odds using ranked set sampling

A. Eftekharian and M. Razmkhah

Statistics & Probability Letters, 2017, vol. 122, issue C, 1-10

Abstract: The kernel estimators of the cumulative distribution function and population odds are proposed based on ranked set sampling scheme. It is shown that the kernel estimators are more efficient than the empirical ones in view of the mean squared error criterion. It is also concluded that ranked set sampling has better performance than simple random sampling, even when the rankings are not perfect.

Keywords: Bounded kernel function; Efficiency; Empirical estimator; Imperfect rankings; Mean integrated squared error; Order statistics (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715216302218
Full text for ScienceDirect subscribers only

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:eee:stapro:v:122:y:2017:i:c:p:1-10

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2016.10.021

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 Catherine Liu ().