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In this study, we adapted the families of estimators from Ünal and Kadilar (2021)  using the exponential function for the population mean in case of non-response for simple random sampling for the estimation of the mean of the population with the RSS (ranked set sampling) method. The equations for the MSE and the bias of the adapted estimators are obtained for RSS and it in theory shows that the proposed estimator is additional efficient than the present RSS mean estimators in the literature. In addition, we support these theoretical results with real COVID-19 real data and conjointly the simulation studies with different distributions and parameters. As a result of the study, it was observed that the efficiency of the proposed estimator was better than the other estimators.

In this study, a new sub-regression type estimator for ranked set sampling (RSS) is proposed based on the idea of a sub-ratio estimator given in Koçyiğit and Kadılar (Commun Stat Theory Methods 1–23, 2022). The proposed unbiased estimator's mean square error is obtained and compared theoretically with other estimators. The theoretical results have been supported by the different simulations and real-life data sets studies and have shown that the proposed estimator is more effective than the estimators in the literature. It is also seen that the number of repetitions in the RSS affected the effectiveness of the sub-estimators.

In this article, a new robust ratio type estimator using the Uk’s redescending M-estimator is proposed for the estimation of the finite population mean in the simple random sampling (SRS) when there are outliers in the dataset. The mean square error (MSE) equation of the proposed estimator is obtained using the first order of approximation and it has been compared with the traditional ratio-type estimators in the literature, robust regression estimators, and other existing redescending M-estimators. A real-life data and simulation study are used to justify the efficiency of the proposed estimators. It has been shown that the proposed estimator is more efficient than other estimators in the literature on both simulation and real data studies.

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