An Estimator for Population Mean Using Power Transformation and Auxiliary Information

AUTHOR AND
AFFILIATION

RAJ. K. GANGELE and SHAILESH K. CHOUBE
Department of Mathematics and Statistics Dr. Sir Hari Singh Gour University Sagar, M.P. (INDIA

KEYWORDS:

Finite population, Study variable, Auxiliary variable, Bias, Variance.

Issue Date:

August 2012

Pages:

ISSN:

2319-8044 (Online) – 2231-346X (Print)

Source:

Vol.24 – No.2

PDF

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DOI:

jusps-A

ABSTRACT:

In this paper we have suggested a family of ratio-cum product estimator when the population coefficient of variation (Cx) alongwith population means (X ) of the auxiliary variable x is known in advance. Its bias and variance to the first degree of approximation are obtained. For an appropriate weight ‘w’ and a good range of -values, it is shown that the suggested estimator is more efficient than the set of estimators viz. usual unbiased estimator, usual ratio and product estimators, Sisodia and Dwivedi’s5 estimator, Bansal and Singh’s1 estimator, Chakraborty’s2 type estimator, Vos’s8 type estimator and Sahai and Ray4 type estimator, some of which are in fact particular members of it.

Copy the following to cite this Article:

RAJ. K. GANGELE and SHAILESH K. CHOUBE, “An Estimator for Population Mean Using Power Transformation and Auxiliary Information”, Journal of Ultra Scientist of Physical Sciences, Volume 24, Issue 2, Page Number , 2016


Copy the following to cite this URL:

RAJ. K. GANGELE and SHAILESH K. CHOUBE, “An Estimator for Population Mean Using Power Transformation and Auxiliary Information”, Journal of Ultra Scientist of Physical Sciences, Volume 24, Issue 2, Page Number , 2016

Available from: http://www.ultrascientist.org/paper/410/


In this paper we have suggested a family of ratio-cum product estimator when the population coefficient of variation (Cx) alongwith population means (X ) of the auxiliary variable x is known in advance. Its bias and variance to the first degree of approximation are obtained. For an appropriate weight ‘w’ and a good range of -values, it is shown that the suggested estimator is more efficient than the set of estimators viz. usual unbiased estimator, usual ratio and product estimators, Sisodia and Dwivedi’s5 estimator, Bansal and Singh’s1 estimator, Chakraborty’s2 type estimator, Vos’s8 type estimator and Sahai and Ray4 type estimator, some of which are in fact particular members of it.