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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
