In the present study we have proposed an improved family of estimators for estimation of population mean using the auxiliary information of median, quartile deviation, Gini's mean difference, Downton's Method, Probability Weighted Moments and their linear combinations with correlation coefficient and coefficient of variation. The performance of the proposed family of estimators is analysed by mean square error and bias and compared with the existing estimators in the literature. By this comparison we conclude that our proposed family of estimators is more efficient than the existing estimators. To support the theoretical results, we also provide the empirical study.
In the present study, we propose the proficient class of estimators of the finite population mean, while incorporating the nonconventional location and nonconventional measures of dispersion with coefficient of variation of the auxiliary variable. Properties associated with the suggested class of improved estimators are derived, and an efficiency comparison with the usual unbiased ratio estimator and other existing estimators under consideration in the present study is established. An empirical study has also been provided to validate the theoretical results. Finally, it is established that the proposed class of estimators of the finite population variance proves to be more efficient than the existing estimators mentioned in this study.
In this study new improved robust estimator has been proposed for precise estimation of finite population variance in simple random sampling by incorporating as auxiliary information of probability weighted moment. Properties associated with proposed estimators are assessed by mean square error and bias through numerical demonstration. We have also provided theoretical efficiency comparison of the study.
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