Title: Efficient classes of estimators for population variance using attribute

Authors: Shashi Bhushan; Anoop Kumar; Sumit Kumar

Addresses: Department of Mathematics and Statistics, Dr. Shakuntala Misra National Rehabilitation University, Lucknow, UP, 226017, India ' Department of Mathematics and Statistics, Dr. Shakuntala Misra National Rehabilitation University, Lucknow, UP, 226017, India ' School of Applied Sciences, K.K. University, Biharsharif, Nalanda, Bihar – 803115, India

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.

Keywords: bias; mean square error; efficiency; auxiliary attribute.

DOI: 10.1504/IJMOR.2022.123124

International Journal of Mathematics in Operational Research, 2022 Vol.22 No.1, pp.74 - 92

Received: 26 Feb 2021
Accepted: 08 Apr 2021
Published online: 30 May 2022 *

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