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EN
The problem of estimation of finite population mean for current occasion in the context of cluster sampling on two successive occasions when there is non-response on both the occasions. Estimators for the current occasion are derived as the particular case when there is non-response on first occasion and second occasion only. A comparison between variances of the estimates is studied. An empirical study is made to study the performance of the proposed strategy.
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EN
This paper considers the problem of estimating the population mean Y of the study variate y using information on auxiliary variate x. We have suggested a generalized version of Bahl and Tuteja (1991) estimator and its properties are studied. It is found that asymptotic optimum estimator (AOE) in the proposed generalized version of Bahl and Tuteja (1991) estimator is biased. In some applications, biasedness of an estimator is disadvantageous. So applying the procedure of Singh and Singh (1993) we derived an almost unbiased version of AOE. A numerical illustration is given in the support of the present study.
EN
This paper advocates the improved separate ratio exponential estimator for population mean of the study variable y using the information based on auxiliary variable x in stratified random sampling. The bias and mean squared error (MSE) of the suggested estimator have been obtained upto the first degree of approximation. The theoretical and numerical comparisons are carried out to show the efficiency of the suggested estimator over sample mean estimator, usual separate ratio and separate product estimator.
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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