Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0378-3758(00)00140-3
Title: Ranked-set sampling with regression-type estimators
Authors: Chen, Z. 
Keywords: 62D05
62G20
62G30
Asymptotic distribution
Concomitant variable
Minimum variance
Ranked set sampling
Regression-type estimator
Issue Date: Jan-2001
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
URI: http://scholarbank.nus.edu.sg/handle/10635/105322
ISSN: 03783758
DOI: 10.1016/S0378-3758(00)00140-3
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