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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

An improved calibration estimator for mean of a stratified population using two auxiliary variables

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pp. 1049–1063Vol. 28Issue 6September 2025DOI: 10.47974/JSMS-1397XML
Received:
09 Apr 2024
Published Online:
14 May 2025
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1397
Pages:
1049–1063

Abstract

Calibration aims to minimize uncertainty in the estimation by ensuring the accuracy of the estimation procedure. The auxiliary information is usually utilized to improve the accuracy of the estimation procedure under the calibration approach. In the present article, the information on two auxiliary variables is utilized to consider a new calibration estimator for the mean of a stratified population. A set of new calibration weights is obtained by minimizing the chi-square type distance function and subject to some calibration constraints based on the auxiliary information. The Taylor linearization technique is used to obtain the expression for the variance of the proposed calibration estimator. The effectiveness of the proposed calibration estimator has been demonstrated through a simulated study. A numerical example using the real data set has also been provided to support the theoretical findings. The analysis shows that the suggested calibration estimator outperforms over the current one in terms of efficiency.

Keywords

Calibration estimatorStratified random samplingPopulation meanTwo auxiliary variablesSimulation study

Subject Classifications

62D05

References

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