Efficient class of ratio cum median estimators for estimating the population median (original) (raw)
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This article suggests an efficient class of estimators of population median of the study variable using an auxiliary variable. Asymptotic expressions of bias and mean square error of the proposed class of estimators have been obtained. Asymptotic optimum estimator has been investigated along with its approximate mean square error. We have shown that proposed class of estimator is more efficient than estimator considered by Srivastava (1967), Gross (1980), Kuk and Mak (1989) Singh et al. (2003b), Al and Chingi (2009) and Singh and Solanki (2013). In addition theoretical findings are supported by an empirical study based on two populations to show the superiority of the constructed estimators over others.
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Pakistan Journal of Statistics and Operation Research, 2013
The present paper deals with a modified ratio estimator for estimation of population mean of the study variable when the population median of the auxiliary variable is known. The bias and mean squared error of the proposed estimator are derived and are compared with that of existing modified ratio estimators for certain known populations. Further we have also derived the conditions for which the proposed estimator performs better than the existing modified ratio estimators. From the numerical study it is also observed that the proposed modified ratio estimator performs better than the existing modified ratio estimators for certain known populations.
AN EFFICIENT ESTIMATOR FOR ESTIMATING FINITEPOPULATION MEAN USING KNOWN MEDIAN OF THE STUDY VARIABLE
The present paper concerns with the estimation of population mean of the study variable by utilizing the known median of the study variable. A generalized ratio type estimator has been proposed for this purpose. The expressions for the bias and mean squared error of the proposed estimator have been derived up to the first order of approximation. The optimum value of the characterizing scalar has also been obtained. The minimum value of the proposed estimator for this optimum value of the characterizing scalar is obtained. A theoretical efficiency comparison of the proposed estimator has been made with the mean per unit estimator, usual ratioestimator of Cochran (1940), usual regression estimator of Watson (1937) ,Bahl and Tuteja estimator (1991), Kadilar (2016) and Subramani (2016) estimators. Through the numerical study, the theoretical findings are validated and it has been found that proposed estimatorperforms better than theexisting estimators.
An Improved Estimator of Population Mean using Information on Median of the Study Variable
International Journal of Mathematics Trends and Technology
The present paper advocates the estimation of population mean of the study variable by utilizing the information on median of the study variable. A generalized ratio type estimator has been proposed for this purpose. The expressions for the bias and mean squared error of the proposed estimator have been derived up to the first order of approximation. The optimum value of the characterizing scalar has also been obtained. The minimum value of the proposed estimator for this optimum value of the characterizing scalar is obtained. A theoretical efficiency comparison of the proposed estimator has been made with the mean per unit estimator, usual ratio of Cochran (1940) and usual regression estimator of Watson (1937),Bahl and Tuteja (1991)estimator, Kadilar (2016) and Subramani (2016) estimators. Theoretical results are supported by the numerical illustration and foundthat proposed estimatorperforms better than theexisting estimators.
An Improved Estimator of Population Mean using Information on Median of the Study Variable 1
The present paper advocates the estimation of population mean of the study variable by utilizing the information on median of the study variable. A generalized ratio type estimator has been proposed for this purpose. The expressions for the bias and mean squared error of the proposed estimator have been derived up to the first order of approximation. The optimum value of the characterizing scalar has also been obtained. The minimum value of the proposed estimator for this optimum value of the characterizing scalar is obtained. A theoretical efficiency comparison of the proposed estimator has been made with the mean per unit estimator, usual ratio of Cochran (1940) and usual regression estimator of Watson (1937),Bahl and Tuteja (1991)estimator, Kadilar (2016) and Subramani (2016) estimators. Theoretical results are supported by the numerical illustration and foundthat proposed estimatorperforms better than theexisting estimators.
ENHANCING EFFICIENCY OF RATIO ESTIMATOR OF POPULATION MEDIAN BY CALIBRATION TECHNIQUES
The use of calibration estimation techniques in survey sampling have been found to improve the precision of estimators. This paper adopts the calibration approach with the assumption that the population median of the auxiliary variable is known to obtain a more efficient ratio-type estimator in estimating population median in stratified sampling. Conditions necessary for efficiency comparison have been obtained which show that the proposed estimator will always perform better than the existing asymptotically unbiased separate estimators in stratified random sampling. Numerical evaluations have been carried out through simulation and real-life data to compliment the theoretical claims. Results from the simulation study carried out under three distributional assumptions, namely the chi square, lognormal and Cauchy distributions with different sample settings showed that the new estimator provided better estimate of the median with greater gain in efficiency. In addition, result from the real-life data further supports the superiority of the proposed estimator over the existing ones considered in this study.
A Family of Median Based Estimators in Simple Random Sampling
In this paper we have proposed a median based estimator using known value of some population parameter(s) in simple random sampling. Various existing estimators are shown particular members of the proposed estimator. The bias and mean squared error of the proposed estimator is obtained up to the first order of approximation under simple random sampling without replacement. An empirical study is carried out to judge the superiority of proposed estimator over others.