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This paper proposes a ratio-cum-product estimator of finite population mean using information on coefficient of variation and coefficient of kurtosis of auxiliary variate. The bias and mean squared error of the proposed estimator are obtained. It has been shown that the proposed estimator is more efficient than the sample mean estimator, usual ratio and(More)
This paper deals with the dual to ratio-cum-product estimator for population mean using known parameters of auxiliary variables. In this paper, dual to ratio-cum-product estimator of Singh and Tailor (2005) has been suggested. The Bias and mean squared error expressions have also been obtained up to the first degree of approximation. Suggested estimator has(More)
In this paper, a ratio-cum-product estimator of population mean in systematic sampling has been proposed using Kadilar and Cingi (2006) estimator. The bias and mean squared error of the proposed estimator has been obtained under large sample approximation. The proposed estimator has been compared with simple mean estimator, usual ratio and product(More)
Separate ratio-type estimators for population mean with their properties are considered. Some separate ratio-type estimators for population mean using known parameters of auxiliary variate are proposed. The bias and mean squared error of the proposed estimators are obtained up to the first degree of approximation. It is shown that the proposed estimators(More)
This paper deals with the problem of estimation of population mean in two-phase sampling. A ratio-product estimator of population mean using known coefficient of kurtosis of two auxiliary variates has been proposed. In fact, it is a two-phase sampling version of Tailor et al. (2010) estimator and its properties are studied. Proposed estimator has been(More)
This paper discusses the problem of estimation of population mean in stratified random sampling. In fact, in this paper, dual to ratio and product type exponential estimators in stratified random sampling have been suggested. The biases and mean squared errors of the suggested estimators haven been obtained up to the first degree of approximation. The(More)