scholarly journals A NEW FAMILY OF ESTIMATORS OF THE POPULATION VARIANCE USING INFORMATION ON POPULATION VARIANCE OF AUXILIARY VARIABLE IN SAMPLE SURVEYS

2016 ◽  
Vol 17 (4) ◽  
pp. 605-630
Author(s):  
Housila P. Singh ◽  
Surya K. Pal
Keyword(s):  
Auxiliary Variable ◽  
Sample Surveys ◽  
Population Variance ◽  
New Family

scholarly journals Estimation of finite population mean using dual auxiliary variable for non-response using simple random sampling

2021 ◽  
Vol 7 (3) ◽  
pp. 4592-4613
Author(s):  
Sohaib Ahmad ◽  
◽  
Sardar Hussain ◽  
Muhammad Aamir ◽  
Faridoon Khan ◽  
...  
Keyword(s):  
Random Sampling ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Sample Mean ◽  
Population Mean ◽  
New Family ◽  
Variable Bias ◽  
Mean Square Errors ◽  
The Given

<abstract><p>This paper addresses the issue of estimating the population mean for non-response using simple random sampling. A new family of estimators is proposed for estimating the population mean with auxiliary information on the sample mean and the rank of the auxiliary variable. Bias and mean square errors of existing and proposed estimators are obtained using the first order of measurement. Theoretical comparisons are made of the performance of the proposed and existing estimators. We show that the proposed family of estimators is more efficient than existing estimators in the literature under the given constraints using these theoretical comparisons.</p></abstract>

On enhanced estimation of population variance using unconventional measures of an auxiliary variable

2020 ◽  
Vol 90 (12) ◽  
pp. 2180-2197
Author(s):  
Muhammad Awais Gulzar ◽  
Muhammad Abid ◽  
Hafiz Zafar Nazir ◽  
Faisal Maqbool Zahid ◽  
Muhammad Riaz
Keyword(s):  
Auxiliary Variable ◽  
Population Variance

scholarly journals A Class of Modified Ratio Estimators for Estimation of Population Variance

2015 ◽  
Vol 11 (1) ◽  
pp. 91-114 ◽  
Author(s):  
J. Subramani ◽  
G. Kumarapandiyan
Keyword(s):  
Mean Squared Error ◽  
Natural Populations ◽  
Auxiliary Variable ◽  
Variance Estimator ◽  
Population Variance ◽  
Study Variable ◽  
Variance Estimators ◽  
Squared Error ◽  
Ratio Estimators ◽  
Better Than

Abstract In this paper we have proposed a class of modified ratio type variance estimators for estimation of population variance of the study variable using the known parameters of the auxiliary variable. The bias and mean squared error of the proposed estimators are obtained and also derived the conditions for which the proposed estimators perform better than the traditional ratio type variance estimator and existing modified ratio type variance estimators. Further we have compared the proposed estimators with that of the traditional ratio type variance estimator and existing modified ratio type variance estimators for certain natural populations.

New alternatives to ratio estimators of population variance in sample surveys

2014 ◽  
Vol 247 ◽  
pp. 255-265
Author(s):  
Hilal A. Lone ◽  
Rajesh Tailor ◽  
Housila P. Singh ◽  
Med Ram Verma
Keyword(s):  
Sample Surveys ◽  
Population Variance ◽  
Ratio Estimators

scholarly journals A Note on Generalized Exponential Type Estimator for Population Variance in Survey Sampling

2015 ◽  
Vol 38 (2) ◽  
pp. 385-397 ◽  
Author(s):  
Javid Shabbir ◽  
Sat Gupta
Keyword(s):  
Exponential Type ◽  
Auxiliary Variable ◽  
Survey Sampling ◽  
Data Sets ◽  
Numerical Comparison ◽  
Order Of Approximation ◽  
Population Variance ◽  
First Order ◽  
Better Than

<p>Recently a new generalized estimator for population variance using information on the auxiliary variable has been introduced by Asghar, Sanaullah &amp; Hanif (2014). In that paper there was some inaccuracy in the bias and MSE expressions. In this paper, we provide the correct expressions for bias and MSE of the Asghar et al. (2014) estimator, up to the first order of approximation. We also propose a new generalized exponential type estimator for population variance which performs better than the existing estimators. Four data sets are used for numerical comparison of efficiencies.</p>

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