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Journal of Statistics and Management Systems

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

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Journal of Statistics and Management Systems
Open Access Research Article

Regression-type ratio estimators of population means using calibration weights

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pp. 751–768Vol. 27Issue 4May 2024DOI: 10.47974/JSMS-975XML
Received:
03 Nov 2021
Accepted:
08 Jun 2022
Published Online:
20 Sep 2023
Article type:
Research Article
Language:
EN
Article no.:
JSMS-975
Pages:
751–768

Abstract

In this paper, some new regression type estimators of population mean with either one or two parameters of the auxiliary variable are proposed for simple random sample without replacement using calibration weighting method. The proposed estimators are obtained by a new calibration weight using known standard deviation of the auxiliary variable in the calibration equation. The mean square error of the proposed estimators was obtained to the first order approximation. In addition, the condition for assessing the efficiency of the proposed estimators with the existing estimators was derived. The efficiency comparison of the proposed estimators was assessed with the estimators proposed by Kadilar & Cingi (2004), Kadilar & Cingi (2006b), Yan & Tian (2010), and Subramani & Kumarapandiyan (2012) using their biases and mean square errors. The results showed that the proposed estimators are more efficient compared to the existing estimators considered in this work.

Keywords

Regression-type ratio estimatorCalibration weightsEfficiencyMean square errorAuxiliary variable

Subject Classifications

62D05

References

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