Modified exponential ratio estimator for ranked set sampling in the presence of tie information and application on COVID-19 real data

Abstract

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