Abstract
The class of chain estimators for a finite population variance using double sampling was presented. The class of chain based estimators was proposed using two auxiliary variables. The auxillary variables were frequently used in survey sampling to increase the efficiency of main estimator. The mean square estimator (MSE) criterion was used for comparing various estimators.
| Original language | English |
|---|---|
| Pages (from-to) | 13-18 |
| Number of pages | 6 |
| Journal | International Journal of Information and Management Sciences |
| Volume | 13 |
| Issue number | 2 |
| Publication status | Published - Jun 2002 |
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Keywords
- Asymptotic Bias
- Double Sampling
- Mean Square Error
ASJC Scopus subject areas
- Management Information Systems
- Industrial and Manufacturing Engineering
Cite this
The class of chain estimators for a finite population variance using double sampling. / Al-Jararha, Jehad; Ahmed, M. S.
In: International Journal of Information and Management Sciences, Vol. 13, No. 2, 06.2002, p. 13-18.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - The class of chain estimators for a finite population variance using double sampling
AU - Al-Jararha, Jehad
AU - Ahmed, M. S.
PY - 2002/6
Y1 - 2002/6
N2 - The class of chain estimators for a finite population variance using double sampling was presented. The class of chain based estimators was proposed using two auxiliary variables. The auxillary variables were frequently used in survey sampling to increase the efficiency of main estimator. The mean square estimator (MSE) criterion was used for comparing various estimators.
AB - The class of chain estimators for a finite population variance using double sampling was presented. The class of chain based estimators was proposed using two auxiliary variables. The auxillary variables were frequently used in survey sampling to increase the efficiency of main estimator. The mean square estimator (MSE) criterion was used for comparing various estimators.
KW - Asymptotic Bias
KW - Double Sampling
KW - Mean Square Error
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UR - http://www.scopus.com/inward/citedby.url?scp=0036606564&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0036606564
VL - 13
SP - 13
EP - 18
JO - International Journal of Information and Management Sciences
JF - International Journal of Information and Management Sciences
SN - 1017-1819
IS - 2
ER -