Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/105426
Title: The optimal ranked-set sampling scheme for inference on population quantiles
Authors: Chen, Z. 
Keywords: Asymptotic normality
Bahadur representation
Optimal sampling design
Quantile
Ranked-set sampling
Issue Date: Jan-2001
Citation: Chen, Z. (2001-01). The optimal ranked-set sampling scheme for inference on population quantiles. Statistica Sinica 11 (1) : 23-37. ScholarBank@NUS Repository.
Abstract: In this article, we consider the design of unbalanced ranked-set sampling in order to achieve certain optimality for inference on quantiles. We first derive the asymptotic properties of the unbalanced ranked-set sample quantiles for any unbalanced ranked-set sampling scheme. Then these properties are employed to develop a methodology for determining optimal ranked-set sampling schemes. In the case of inference on a single quantile, the optimal scheme results in an estimator of the quantile which is asymptotically unbiased and with minimum variance among all ranked-set sample (balanced or unbalanced) quantiles. The striking feature of the methodology is that it is distribution-free. The optimal schemes for inference on certain quantiles are computed. Some simulation studies are reported.
Source Title: Statistica Sinica
URI: http://scholarbank.nus.edu.sg/handle/10635/105426
ISSN: 10170405
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

45
checked on Jan 20, 2022

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.