Modified Exponential Ratio Estimator for Ranked Set Sampling in the Presence of Tie Information and Application on COVID-19 Real Data

In this article, we develop theisotonic ratio mean estimator proposed by Kocyigit and Kadilar (2020) withinthe case of tie information in the Ranked Set Sampling (RSS). In addition, wetake into consideration the population size being large and small. We offer ageneralized exponential ratio estimator using the modified isotonic estimatorfor this situation. Simulation results show that the proposed estimator is moreefficient than the estimators in the literature. In the real data study, the 34regions of China since China is the country where the pandemic started aretaken as a small population, and the data of 212 countries on a world scale istaken as a large population, then the estimators are calculated. In the realdata study, the proposed estimator gives better results than the existingestimators thus the results in the simulation study are similar to the real datastudy.