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September 5, 2025Open Access

Estimation of Population Mean in Domains of Study Under Double Sampling: A Calibration Technique for Subsampling the Nonrespondents

Puntos clave

The study shows that condition A provides more efficient estimates of population mean compared to condition B.
Findings indicate that proposed estimators yield unbiased estimates of population mean under double sampling conditions.
Using empirical data, variance and MSE were evaluated, demonstrating the superior efficiency of the new estimator.
This work highlights the necessity of calibration in sub-sampling non-respondents to improve accuracy in survey estimates.

Resumen

The application of calibration in domain estimation with subsampling the non-respondents is the major emphasis of this work. The detrimental impact that result from a researcher’s incapacity to gather data from every elemen-tary unit in a certain demographic or from respondents’ partial or complete unwillingness to supply the necessary information is still a problem in sample surveys. In view of the mentioned problem, this work becomes imperative as it utilizes the concept of calibration in double sampling. In order to increase the accuracy of such estimates by subsampling the non-respondents, it becomes important to close the gap between the loss of survey data and establishing a reliable estimate in the research areas. This formulation is subject to two conditions, when the auxiliary variable is free from non-response (condition A) and when the auxiliary variable is not free from non-response (condition B). And it has been demonstrated that the suggested estimators are unbiased. Two populations from actual data were examined in terms of variance/MSE and percentage relative efficiency (PRE) as part of the empirical study. Ad-ditionally, two non-response scenarios-one with uniform rates and the other with non-uniform rate were taken into account. The findings demonstrated that although the two conditions yield the same population mean estimates, condition A is more efficient than condition B. The cases, on the other hand, produce different estimates of population mean and variance/MSE. Compared to the existing estimators used in this work, the proposed estimator has proven to be more effective in providing accurate estimates of the population mean in the domains. This is evident from the efficiency comparisons and empirical investigations of the proposed estimator and the existing estimators that the proposed estimator are capable of producing efficient estimates in domains of study when subsampling the nonrespondents.

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