Some Improved Class of Ratio Estimators for Finite Population Variance with the Use of Known Parameters

In this paper, we proposed some improved class of ratio estimators for finite population variance with the use of known parameters. The proposed estimators are obtained by transforming both the sample variances of the study and auxiliary variables, as well as the use of known parameters. The Mean Square Error of the proposed estimators have been obtained and the conditions for their efficiency over some existing variance estimators have been established. The present family of finite variance estimator, having obtaining the optimal values of the constants, exhibit significant improvement over the estimators considered in the study. The empirical study is also conducted to support the theoretical results and the results revealed that the suggested estimators are more efficient

An Efficient Logarithmic Ratio Type Estimator of Finite Population Mean under Simple Random Sampling

The use of auxiliary information has become indispensable for improving the exact of estimators of population parameters like the mean and variance of the variable under study. A great variety of the techniques such as the ratio, product, and regression methods of estimation are commonly known in this esteem. In this paper, we propose an efficient logarithmic ratio type estimator for finite population mean estimation under simple random sampling. The expression for the bias and mean squared error (MSE) of the proposed estimator is obtained up to the first order of approximation. The conditions under which the proposed estimator is more efficient than the existing ones are established. An empirical study using three data sets is also conducted to validate the theoretical findings and the results revealed that the suggested estimator is better than the existing estimators considered in the study

An Efficient Logarithmic Ratio Type Estimator of Finite Population Mean under Simple Random Sampling

The use of auxiliary information has become indispensable for improving the exact of estimators of population parameters like the mean and variance of the variable under study. A great variety of the techniques such as the ratio, product, and regression methods of estimation are commonly known in this esteem. In this paper, we propose an efficient logarithmic ratio type estimator for finite population mean estimation under simple random sampling. The expression for the bias and mean squared error (MSE) of the proposed estimator is obtained up to the first order of approximation. The conditions under which the proposed estimator is more efficient than the existing ones are established. An empirical study using three data sets is also conducted to validate the theoretical findings and the results revealed that the suggested estimator is better than the existing estimators considered in the study

An Efficient Exponential Estimator of Population Mean in the Presence of Median of the Study Variable

Survey sampling practitioners have been working on efficiency improvement and bias reduction in finite population parameter estimation. We proposed an exponential estimator of population mean in the presence of median of study variable. The bias and mean square error of the proposed estimator were obtained using Taylor series method. The relative performance of the proposed estimators with respect to conventional and some existing estimators were assessed using three (3) natural dataset information. The novel median based estimator perform better than the conventional, usual mean, ratio, regression and other existing estimators considered in the study have been established. The empirical results shown that the proposed estimator is more efficient than the conventional and some existing estimators considered in the study

A Modified Ratio Estimator in the Presence of Tri-Mean and Interquartile Range for the Estimation of Population Variance

In this paper, a modified ratio estimator in the presence of tri-mean and interquartile range, for estimating the population variance is proposed. Having studied the estimator proposed by Yadav et al. (2017) where they made use of information on tri-mean and inter-quartile range of the auxiliary variable to improve the estimate and efficiency of the variance estimator for estimating the population variance. The bias and mean squared error of the proposed estimator were derived up to first order of approximation and conditions for which the proposed estimator more efficient than other estimators considered in the study were also established. Numerical illustration was conducted using Murthy (1967) and Singh and Chaudhary (1986) datasets. It was shown that the proposed modified ratio estimator perform better than some existing related ratio estimators