Efficient classes of estimators for population variance using attribute. (23rd May 2022)

• Record Type: Journal Article
• Authors: Bhushan, Shashi; Kumar, Anoop; Kumar, Sumit
• Is Part Of: International journal of mathematics in operational research. Volume 22:Number 1(2022)
• Page Start: 74
• Page End: 92
• Publication Date: 2022-05-23
• Subjects: bias -- mean square error -- efficiency -- auxiliary attribute; Operations research -- Mathematical models -- Periodicals; Operations research -- Mathematics -- Periodicals; Decision making -- Mathematical models -- Periodicals; 658.4033
• Languages: English
• ISSNs: 1757-5850

Abstract

This article deals with the problem of estimating population variance of study variable by using information on auxiliary attribute in simple random sampling. We have adapted the procedure of Kadilar and Cingi (2006) and established some efficient classes of estimators for population variance. The performance of the proposed estimators have been assessed by an empirical study using two real populations and the results demonstrate that the proposed estimators present an extensively greater efficiency when compared with the usual mean estimator, classical ratio, regression and exponential estimators suggested by Singh and Kumar (2011), Singh and Malik (2014) estimators, Zaman and Kadilar (2019) type estimators, Zaman (2020) type estimator and Cekim and Kadilar (2020) type estimator.