RATIO ESTIMATORS FOR THE CO-EFFICIENT OF VARIATION IN A FINITE POPULATION

Archana V

Abstract


The Co-efficient of variation (C.V) is a relative measure of dispersion and is free from unit of measurement. Hence it is widely used by the scientists in the disciplines of agriculture, biology, economics and environmental science. Although a lot of work has been reported in the past for the estimation of population C.V in infinite population models, they are not directly applicable for the finite populations. In this paper we have proposed six new estimators of the population C.V in finite population using ratio and product type estimators. The bias and mean square error of these estimators are derived for the simple random sampling design. The performance of the estimators is compared using a real life dataset. The ratio estimator using the information on the population C.V of the auxiliary variable emerges as the best estimator

Keywords


Sampling

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DOI: http://dx.doi.org/10.18187/pjsor.v7i2.196

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Title

RATIO ESTIMATORS FOR THE CO-EFFICIENT OF VARIATION IN A FINITE POPULATION

Keywords

Sampling

Description

The Co-efficient of variation (C.V) is a relative measure of dispersion and is free from unit of measurement. Hence it is widely used by the scientists in the disciplines of agriculture, biology, economics and environmental science. Although a lot of work has been reported in the past for the estimation of population C.V in infinite population models, they are not directly applicable for the finite populations. In this paper we have proposed six new estimators of the population C.V in finite population using ratio and product type estimators. The bias and mean square error of these estimators are derived for the simple random sampling design. The performance of the estimators is compared using a real life dataset. The ratio estimator using the information on the population C.V of the auxiliary variable emerges as the best estimator

Date

2011-04-27

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol 7. No. 2, July 2011



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810