Estimation of Location and Scale Parameters of a Logistic Distribution Using a Ranked Set Sample

Kin Lam, Bimal K. Sinha and Zhong Wu

Chapter 16 in Statistical Theory and Applications, 1996, pp 187-197 from Springer

Abstract: Abstract In situations where the experimental or sampling units in a study can be more easily ranked than quantified, McIntyre (1952) proposed that the mean of n units based on a ranked set sample (RSS) be used to estimate the population mean. He observed that it provides an unbiased estimator with a smaller variance compared to the mean of a simple random sample (SRS) of the same size n. McIntyre’s concept of RSS is essentially nonparametric in nature in that the underlying population distribution is assumed to be completely unknown. Here we explore the concept of RSS to estimate the location, scale and quantiles of a logistic distribution. It turns out that the use of RSS and its suitable modifications result in much improved estimators compared to the use of an SRS in all the three cases.

Keywords: Best linear unbiased estimator; order statistics; ranked set sample; quantiles; sample median (search for similar items in EconPapers)
Date: 1996
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-1-4612-3990-1_16

Ordering information: This item can be ordered from
http://www.springer.com/9781461239901

DOI: 10.1007/978-1-4612-3990-1_16

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().