The New Sub-regression Type Estimator in Ranked Set Sampling

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 theoretical...

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Detalles Bibliográficos
Autores principales: Koçyiğit, Eda Gizem, Rather, Khalid Ul Islam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9974047/
https://www.ncbi.nlm.nih.gov/pubmed/36875336
http://dx.doi.org/10.1007/s42519-023-00324-9
Descripción
Sumario: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.

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