Please use this identifier to cite or link to this item:
|Title:||Ranked-set sampling with regression-type estimators|
Ranked set sampling
|Citation:||Chen, Z. (2001-01). Ranked-set sampling with regression-type estimators. Journal of Statistical Planning and Inference 92 (1-2) : 181-192. ScholarBank@NUS Repository. https://doi.org/10.1016/S0378-3758(00)00140-3|
|Abstract:||Ranked set sampling (RSS) is a sampling scheme to reduce cost and increase efficiency in situations where the measurement of a survey variable is costly and/or time-consuming but ranking of sampled items relating to the survey variable can be easily done by certain other means. When a concomitant variable is readily available, the concomitant variable can be employed to aid in both sampling and estimation. Regression-type estimators making use of concomitant variables have been proposed in the literature. In this article, we study further the properties of the regression-type estimators and propose a modified RSS regression estimator which improves the available estimators. Comparison among the proposed and available estimators are made both theoretically and by simulation. Asymptotic distribution of the regression-type estimators are established and hence construction of confidence intervals and hypothesis testing based on these estimators are made possible. © 2001 Elsevier Science B.V.|
|Source Title:||Journal of Statistical Planning and Inference|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 6, 2018
WEB OF SCIENCETM
checked on Nov 21, 2018
checked on Nov 23, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.