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

Koçyiğit, Eda Gizem; Kadilar, Cem

doi:10.47974/JSMS-925

Open Access Research Article

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

^*Eda Gizem KoçyiğitCorresponding authoreda.kocyigit@deu.edu.trDepartment of StatisticsDokuz Eylul UniversityIzmir, Turkey0000-0002-0774-1376View full profile → , Cem Kadilarkadilar@hacettepe.edu.trDepartment of StatisticsHacettepe UniversityAnkara, Turkey0000-0003-4950-9660View full profile →

* Corresponding author · click or hover a name for details

pp. 515–534Vol. 27Issue 3March 2024DOI: 10.47974/JSMS-925XML

Received:: 20 Jul 2021
Accepted:: 03 Feb 2022
Published Online:: 30 Mar 2024
Article type:: Research Article
Language:: EN
Article no.:: JSMS-925
Pages:: 515–534

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.

Keywords

Ranked set sampling Exponential ratio estimator Tie Isotonic estimator COVID-19

Subject Classifications

62D0562F03

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