Improved estimation of the mean through regressed exponential estimators based on sub-sampling non-respondents

The present study concerns the issue of estimating the population mean and presents novel and improved regressed exponential estimators using different parameters of an auxiliary character based on sub-sampling non-respondents. The bias and mean square error (MSE) of the proposed estimators for the most pragmatic simple random sampling without replacement (SRSWOR) scheme have been derived up to the first order of approximation (i.e. the expression containing errors up to the power of two so that the expectation comes only in terms of the mean, variance and covariance). The optimum value of the MSE of the estimators is found, along with the necessary conditions for optimising the MSE. The effectiveness of the suggested estimators, outperforming the existing ones in terms of their MSE, has been studied theoretically, while the empirical as well as the simulation studies have confirmed these findings.

Authors

Publication Details

Journal
Statistics in Transition New Series
Published
2024-09-04
DOI
https://doi.org/10.59170/stattrans-2024-030
Citations
1
Primary Topic
Survey Sampling and Estimation Techniques
Type
article
Field-Weighted Citation Impact
0.71
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article

Improved estimation of the mean through regressed exponential estimators based on sub-sampling non-respondents

R. R. Sinha, Khanna Bharti
1 citations
Statistics in Transition New Series
Survey Sampling and Estimation Techniques
0.71
article

Improved estimation of the mean through regressed exponential estimators based on sub-sampling non-respondents

R. R. Sinha, Khanna Bharti
article en
1 citations

Abstract

The present study concerns the issue of estimating the population mean and presents novel and improved regressed exponential estimators using different parameters of an auxiliary character based on sub-sampling non-respondents. The bias and mean square error (MSE) of the proposed estimators for the most pragmatic simple random sampling without replacement (SRSWOR) scheme have been derived up to the first order of approximation (i.e. the expression containing errors up to the power of two so that the expectation comes only in terms of the mean, variance and covariance). The optimum value of the MSE of the estimators is found, along with the necessary conditions for optimising the MSE. The effectiveness of the suggested estimators, outperforming the existing ones in terms of their MSE, has been studied theoretically, while the empirical as well as the simulation studies have confirmed these findings.

Statistics in Transition New SeriesVol. 25(3)
Openalex Percentile: Top 27%
Survey Sampling and Estimation Techniques
0.71
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Improved estimation of the mean through regressed exponential estimators based on sub-sampling non-respondents — R. R. Sinha, Khanna Bharti · Statistics in Transition New Series (2024) | TGRS Research Map | TGRS