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Best Linear Unbiased Estimators of Population Mean on Current Occasion in Two-Occasion Rotation Patterns

100%
Singh G. N., Prasad S.
Statistics in Transition new series
|
2013
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vol. 14
|
issue 1
57-74
EN
Best linear unbiased estimators have been proposed to estimate the population mean on current occasion in two-occasion successive (rotation) sampling. Behavior of the proposed estimators have been studied and their respective optimum replacement policies are discussed. Empirical studies are carried out to examine the performance of the proposed estimators and consequently the suitable recommendations are made.
2
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Some Rotation Patterns in Two-Phase Sampling

86%
Singh G. N., Prasad S.
Statistics in Transition new series
|
2011
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vol. 12
|
issue 1
25-44
EN
A problem related to the estimation of population mean on the current occasion using two-phase successive (rotation) sampling on two occasions has been considered. Two-phase ratio, regression and chain-type estimators for estimating the population mean on current (second) occasion have been proposed. Properties of the proposed estimators have been studied and their respective optimum replacement policies are discussed. Estimators are compared with the sample mean estimator, when there is no matching and the natural optimum estimator, which is a linear combination of the means of the matched and unmatched portions of the sample on the current occasion. Results are demonstrated through empirical means of comparison and suitable recommendations are made.
3
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Ratio-To-Regression Estimator In Successive Sampling Using One Auxiliary Variable

86%
Ralte Z., Das G.
Statistics in Transition new series
|
2015
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vol. 16
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issue 2
183-202
EN
The problem of estimation of finite population mean on the current occasion based on the samples selected over two occasions has been considered. In this paper, first a chain ratio-to-regression estimator was proposed to estimate the population mean on the current occasion in two-occasion successive (rotation) sampling using only the matched part and one auxiliary variable, which is available in both the occasions. The bias and mean square error of the proposed estimator is obtained. We proposed another estimator, which is a linear combination of the means of the matched and unmatched portion of the sample on the second occasion. The bias and mean square error of this combined estimator is also obtained. The optimum mean square error of this combined estimator was compared with (i) the optimum mean square error of the estimator proposed by Singh (2005) (ii) mean per unit estimator and (iii) combined estimator suggested by Cochran (1977) when no auxiliary information is used on any occasion. Comparisons are made both analytically as well as empirically by using real life data.
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