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Ratio estimators of the population mean with missing values using ranked set sampling
Author(s) -
AlOmari A. I.,
Bouza C. N.
Publication year - 2015
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2286
Subject(s) - estimator , statistics , mathematics , population mean , simple random sample , mean squared error , imputation (statistics) , missing data , ratio estimator , population , sampling (signal processing) , mean square , econometrics , bias of an estimator , minimum variance unbiased estimator , computer science , demography , filter (signal processing) , sociology , computer vision
The existence of missing observations (MO) is commonly solved by using imputation methods. There are ratio‐based methods for estimating the population mean, while using simple random sampling (SRS), when MO are present. Considering the existence of MO and using of ranked set sampling, we develop a study of the estimation of a population mean using ratio‐based methods. The mean square errors, bias, and gain in accuracy formulas of the suggested estimators are derived. The suggested estimators are compared with their SRS counterpart both theoretically and numerically. Copyright © 2014 John Wiley & Sons, Ltd.