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- MakaleAçık Erişim2026Scopus SJRQ2A robust calibration-based estimator in presence of non-sensitive and sensitive variables using moments in sample surveys
Yadav, Vinay Kumar; Bulut, Hasan; Zaman, Tolga
In survey sampling, the calibration approach is among the most widely used techniques to improve estimator efficiency. In this study, we introduced robust regression approaches into calibrated exponential ratio-type estimators for finite population mean, which helps to ensure better estimation precision when outliers are present in the data. In particular, using stratified random sampling, we introduce the first three moments of the origin of an auxiliary variable in the calibration approach. Second-and third-order moments are also considered by using exponential mean-type estimators. This technique is further modified for study involving sensitive variables, especially in research on personal relationships and intimate health where the respondent may under-report because of privacy concerns. Biased answers are often obtained when questions such as “Have you ever engaged in unprotected sex?" or “Have you ever been tested for a sexually transmitted infection?" By reducing these biases, our strong calibration estimators improve the accuracy of the study. In a simulation study, the data is randomly generated for each stratum and outliers are introduced at 5%, 10% and 25% by randomly selecting a specified proportion of the generated data and multiplying those values by a factor of 10. The findings of our suggested robust estimators perform better than those already in use, especially when it comes to managing sensitive data and outliers, which makes them more effective for real-world use.
- MakaleYalnız künye2026Scopus SJRQ3Optimal approach to enhancing population mean estimation using auxiliary parameters
Kumar, Surendra; Bari, Tarushree; Yadav, Subhash Kumar; Zaman, Tolga
This study presents an efficient method for estimating the population mean of a study variable using a Searls exponential ratio estimator. The approach is based on simple random sampling and leverages known population parameters of an auxiliary variable for enhanced estimation of population mean. Using large sample approximations up to the first order, the expressions for the bias and mean squared error of the proposed estimator are derived. To evaluate its performance, the estimator is compared conceptually and mathematically with several existing estimators. Theoretical results are further validated through both simulated and real-world data sets. Findings from these evaluations demonstrate that the proposed estimator consistently outperforms many competing estimators, particularly at higher population mean values. Therefore, it holds promise for improving population mean estimation across a range of practical applications.
AtıfWoS1İncele - MakaleYalnız künye2026Scopus SJRQ3Improved generalized ratio estimators of population mean using auxiliary variables
Yadav, Subhash Kumar; Arya, Diksha; Sharma, Prayas; Zaman, Tolga
The Quest for the improved estimators of population mean is a continuous process to estimate it more efficiently and more closely to the true population mean of the study variable. There are various estimators of population mean in the literature, which estimate it efficiently, but still there is space for more efficient estimators. For improved estimation of population mean of primary variable, we suggest six different searls type ratio estimators utilizing the known auxiliary parameters. We derive the expressions for the biases and the mean squared errors of the introduced estimators for an approximation of degree one. The optimal values of the Searls characterizing constants, which minimize the mean squared errors of the introduced estimators, are obtained. The least value of the mean squared error of the proposed estimators for these optimal values of the characterizing scalars is also acquired. The efficiency of the proposed estimators is compared theoretically with the competing estimators of population mean through their mean squared errors. We derive the efficiency conditions of the proposed estimator for which it is more efficient than the estimators of population mean in competition. These efficiency conditions of the proposed estimators are verified through empirical data and the improvement over the competing estimators is calculated based on the minimum values of the mean squared errors. It is evident from the numerical study that the proposed estimator has the least mean squared error and the highest percentage relative efficiency in comparison to competing estimators. Thus, the proposed estimators are recommended for applications in different areas of applications.
AtıfScopus2İncele - MakaleAçık Erişim2024AN EFFICIENT FAMILY OF RATIO TYPE ESTIMATORS FOR SIMPLE RANDOM SAMPLING
Yadav, Subhash Kumar; Arya, Diksha; Koc, Tuba; Zaman, Tolga
The study provides an enhanced estimation of the population mean using known information on an auxiliary variable. An enhanced class of estimators is suggested for the same. The proposed estimator's bias and mean squared error (MSE) are calculated up to the first order of approximation. The optimum values of the characterizing constants are obtained by minimizing the MSE of the proposed estimator. The minimum MSE and the bias values are achieved by optimising the characterizing scalar. The MSE of the proposed estimator has also been compared both conceptually and empirically with the MSEs of competing estimators. Real and simulated data sets are adopted to verify the theoretical prerequisites for the proposed estimator's greater efficiency over competing estimators. The most efficient estimator is recommended for practical utility in different areas of applications and the suggested estimator filfills the requirement.
AtıfWoS6İncele