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Modified Estimators of Population Variance in Presence of Auxiliary Information

100%
Tailor R., Sharma B.
Statistics in Transition new series
|
2012
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vol. 13
|
issue 1
37-46
EN
This paper proposes estimator of population variance using information on known parameters of auxiliary variable. The variances of the proposed estimators are obtained. It has been shown that using modified sampling fraction the proposed estimators are more efficient than the usual unbiased estimator of population variance and usual ratio estimator for population variance under certain given conditions. Empirical study is also carried out to demonstrate the merits of the proposed estimators of population variance over other estimators considered in this paper.
2
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Improved separate ratio and product exponential type estimators in the case of post-stratification

100%
Lone H. A., Tailor R.
Statistics in Transition new series
|
2015
|
vol. 16
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issue 1
53–64
EN
This paper addressed the problem of estimation of finite population mean in the case of post-stratification. Improved separate ratio and product exponential type estimators in the case of post-stratification are suggested. The biases and mean squared errors of the suggested estimators are obtained up to the first degree of approximation. Theoretical and empirical studies have been done to demonstrate better efficiencies of the suggested estimators than other considered estimators.
3
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A Ratio-Cum-Product Estimator of Finite Population Mean in Systematic Sampling

81%
Tailor R., Jatwa N. K., Singh H. P.
Statistics in Transition new series
|
2014
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vol. 15
|
issue 3
391-398
EN
In this paper we consider the problem of estimation of population mean using information on two auxiliary variables in systematic sampling. We have extended Singh (1967) estimator for estimation of population mean in systematic sampling. We have derived the expressions for the bias and mean squared error of the suggested estimator up to the first degree of approximation. We have compared the suggested estimator with existing estimators and obtained the conditions under which the suggested estimator is more efficient. An empirical study has been carried out to demonstrate the performance of the suggested estimator.
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