Abstract
Classical estimators often suffer from inefficiency when data is contaminated with outliers, leading to skewed results. To overcome this issue, we propose robust estimators for estimating the population mean in various ranked set sampling scenarios. Our research introduces an innovative technique that enhances Zaman estimators under simple random sampling (SRS) and extends these advancements to ranked set sampling (RSS) and median-ranked set sampling (MRSS) designs. Real and simulated datasets, incorporating outliers, are employed to compare the performance of the proposed robust estimator using robust regression methods, that is, LAD, LMS, LTS, Huber-M, Hampel-M, Turkey-M, and Huber-MM, against classical and Zaman estimators. The results demonstrate the superior performance of our robust regression-type estimator in effectively handling outliers.
Acknowledgments
We are thankful to IIT (ISM) Dhanbad for providing the financial and infrastructural support to accomplish the present work. Additionally, the authors would like to express their gratitude to the anonymous referees and Associate Editors for their constructive comments that improved the quality of this paper. Their insightful and helpful feedback has made a significant difference in the manuscript’s current state.
Citation
M. K. Pandey. G. N. Singh. A. Bandyopadhyay. "Efficiency study of a robust regression-type estimator for population mean under different ranked set sampling methods with outlier handling." Braz. J. Probab. Stat. 38 (2) 232 - 252, June 2024. https://doi.org/10.1214/24-BJPS602
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