Efficient Estimator of Population Variance Using Coefficient of Kurtosis and Population Mean of Auxiliary Variable


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Authors

  • S. K. Yadav Department of Mathematics and Statistics (A Centre of Excellence), Dr.R M L Avadh University, Faizabad, U.P., India
  • Ravendra Kumar Department of Mathematics, Dr Ram Manohar Lohiya Government Degree College, Aonla, Bareilly, U.P., India
  • Shakti Kumar CESP School of Social Sciences, Jawahar Lal Nehru University, New Delhi, India
  • Shailendra Verma Department of Business Administration and Interprenureship, Dr.R M L Avadh University, Faizabad, U.P., India
  • Shailendra Kumar Department of Microbiology, Dr.R M L Avadh University, Faizabad, U.P., India

Keywords:

Main variable, auxiliary variable, bias, mean squared error, efficiency

Abstract

In the present paper an efficient estimator of population variance of study variable has been proposed using knowledge of coefficient of kurtosis and the population mean of the auxiliary variable. The bias and the mean squared error of the proposed variance have been obtained up to the first order of approximation. The optimum value of the characterizing scalar, which minimizes the mean squared error, has been obtained. The minimum value of the mean squared error has been obtained for this optimum value of the characterizing scalar. A comparison has been done with the mentioned existing estimators of population variance. An empirical study is also carried out to judge the performance of the proposed estimator along with the other estimators of population variance.

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Published

30-12-2015

How to Cite

S. K. Yadav, Ravendra Kumar, Shakti Kumar, Shailendra Verma, & Shailendra Kumar. (2015). Efficient Estimator of Population Variance Using Coefficient of Kurtosis and Population Mean of Auxiliary Variable. International Journal of Mathematics And Its Applications, 3(4 - F), 85–91. Retrieved from https://ijmaa.in/index.php/ijmaa/article/view/537

Issue

Vol. 3 No. 4 - F (2015)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.