scholarly journals Median Based Modified Ratio Estimators with Known Quartiles of an Auxiliary Variable

2014 ◽  
Vol 13 (1) ◽  
pp. 234-254
Author(s):  
Jambulingam Subramani ◽  
G Prabavathy
Keyword(s):  
Auxiliary Variable ◽  
Ratio Estimators

scholarly journals Modified ratio estimators of population mean using linear combination of co-efficient of skewness and quartile deviation

2013 ◽  
Vol 31 (1) ◽  
pp. 39 ◽  
Author(s):  
M. Iqbal Jeelani ◽  
S. Maqbool
Keyword(s):  
Linear Combination ◽  
Auxiliary Variable ◽  
Degree Of Approximation ◽  
Linear Combinations ◽  
Study Variable ◽  
Population Mean ◽  
Mean Squared Errors ◽  
Ratio Estimators ◽  
Coefficient Of Skewness ◽  
Better Than

The present paper deals with the estimation of population mean of the study variable using the linear combination of known population values of coefficient of skewness and quartile deviation of auxiliary variable. Two modified ratio estimators for estimation of population mean of the study variable involving the above linear combinations are being used. Mean squared errors and biases up to the first degree of approximation are derived and compared with the proposed modified ratio estimators. The proposed modified ratio estimators perform better than the existing ratio estimators. The empirical study has been carried out in support of the results.

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.

scholarly journals Ratio Estimator in Adaptive Cluster Sampling without Replacement of Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Nipaporn Chutiman ◽  
Monchaya Chiangpradit
Keyword(s):  
Auxiliary Variable ◽  
Cluster Sampling ◽  
Ratio Estimator ◽  
Adaptive Cluster Sampling ◽  
Population Total ◽  
Sampling Without Replacement ◽  
Numerical Examples ◽  
Ratio Estimators

In this paper, we study the estimators of the population total in adaptive cluster sampling by using the information of the auxiliary variable. The numerical examples showed that the ratio estimator in adaptive cluster sampling without replacement of networks is more efficient than the ratio estimators in adaptive cluster sampling without replacement of units.

scholarly journals Enhancing the Mean Ratio Estimators for Estimating Population Mean Using Non-Conventional Location Parameters

2016 ◽  
Vol 39 (1) ◽  
pp. 63-79 ◽  
Author(s):  
Muhammad Abid ◽  
Nasir Abbas ◽  
Hafiz Zafar Nazir ◽  
Zhengyan Lin
Keyword(s):  
Empirical Study ◽  
Coefficient Of Variation ◽  
Auxiliary Variable ◽  
Mean Square ◽  
Linear Combinations ◽  
Population Correlation ◽  
Location Parameters ◽  
The Mean ◽  
Ratio Estimators ◽  
Mean Square Errors

<p>Conventional measures of location are commonly used to develop ratio estimators. However, in this article, we attempt to use some non-conventional location measures. We have incorporated tri-mean, Hodges-Lehmann, and mid-range of the auxiliary variable for this purpose. To enhance the efficiency of the proposed mean ratio estimators, population correlation coefficient, coefficient of variation and the linear combinations of auxiliary variable have also been exploited. The properties associated with the proposed estimators are evaluated through bias and mean square errors. We also provide an empirical study for illustration and verification.</p>

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