Estimating the parameter of selected uniform population under the squared log error loss function
K. R. Meena,
Mohd. Arshad and
Aditi Kar Gangopadhyay
Communications in Statistics - Theory and Methods, 2018, vol. 47, issue 7, 1679-1692
Abstract:
Let π1, …, πk be k (⩾ 2) independent populations, where πi denotes the uniform distribution over the interval (0, θi) and θi > 0 (i = 1, …, k) is an unknown scale parameter. The population associated with the largest scale parameter is called the best population. For selecting the best population, We use a selection rule based on the natural estimators of θi, i = 1, …, k, for the case of unequal sample sizes. Consider the problem of estimating the scale parameter θL of the selected uniform population when sample sizes are unequal and the loss is measured by the squared log error (SLE) loss function. We derive the uniformly minimum risk unbiased (UMRU) estimator of θL under the SLE loss function and two natural estimators of θL are also studied. For k = 2, we derive a sufficient condition for inadmissibility of an estimator of θL. Using these condition, we conclude that the UMRU estimator and natural estimator are inadmissible. Finally, the risk functions of various competing estimators of θL are compared through simulation.
Date: 2018
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2017.1324986 (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:47:y:2018:i:7:p:1679-1692
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2017.1324986
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 ().