Improved Estimation Procedure for Population Coefficient of Variation Using Calibrated Weights under Stratified Successive Sampling in the Presence of Non-Response and Measurement Errors

In this paper, we introduce estimators for the population coefficient of variation within a two-occasion, stratified, successive sampling framework, aiming to mitigate the impact of non-response and measurement errors. We derive calibrated weights for the strata and thoroughly examine the properties of the proposed estimator through comprehensive numerical and simulation studies. Furthermore, we provide valuable recommendations for survey statisticians, guiding them on effective applications in real-world survey scenarios. By addressing the challenges of non-response and measurement errors within a stratified sampling approach, our proposed estimators aim to enhance the accuracy and precision of coefficient of variation estimates, ensuring more precise and accurate results.

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Publication Details

Journal
Scientia Iranica
Published
2024-10-13
DOI
https://doi.org/10.24200/sci.2024.63180.8276
Primary Topic
Survey Sampling and Estimation Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Improved Estimation Procedure for Population Coefficient of Variation Using Calibrated Weights under Stratified Successive Sampling in the Presence of Non-Response and Measurement Errors

Arnab Bandyopadhyay, M. K. Pandey, G. N. Singh
Scientia Iranica
Survey Sampling and Estimation Techniques
article

Improved Estimation Procedure for Population Coefficient of Variation Using Calibrated Weights under Stratified Successive Sampling in the Presence of Non-Response and Measurement Errors

Arnab Bandyopadhyay, M. K. Pandey, G. N. Singh
article en

Abstract

In this paper, we introduce estimators for the population coefficient of variation within a two-occasion, stratified, successive sampling framework, aiming to mitigate the impact of non-response and measurement errors. We derive calibrated weights for the strata and thoroughly examine the properties of the proposed estimator through comprehensive numerical and simulation studies. Furthermore, we provide valuable recommendations for survey statisticians, guiding them on effective applications in real-world survey scenarios. By addressing the challenges of non-response and measurement errors within a stratified sampling approach, our proposed estimators aim to enhance the accuracy and precision of coefficient of variation estimates, ensuring more precise and accurate results.

Scientia IranicaVol. 0(0)
Indian Institute of Technology Dhanbad (IN)
Indian Institute of Technology (Indian School of Mines), Dhanbad
Openalex Percentile: Top 73%
Survey Sampling and Estimation Techniques
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