Predictive estimation for mean under median ranked set sampling: an application to COVID-19 data

Sweta Shukla (), Abhishek Singh () and Gajendra K. Vishwakarma ()
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Sweta Shukla: Institute of Applied Sciences and Humanities, GLA University
Abhishek Singh: Department of Mathematics & Statistics, Dr. Vishwanath Karad MIT World Peace University
Gajendra K. Vishwakarma: Indian Institute of Technology (ISM) Dhanbad

Indian Journal of Pure and Applied Mathematics, 2025, vol. 56, issue 1, 218-229

Abstract: Abstract In this manuscript, we have implemented predictive estimators under the median ranked set sampling (MRSS). Alike predictive estimators under simple random sampling (SRS), MRSS has also shown that predictive estimators based on unbiased, ratio and regression estimators are same as natural unbiased, ratio and regression estimators under MRSS. While the predictive estimator based on product estimator is not the same as a natural product estimator under MRSS, although has the same MSE but differs by bias. Predictive estimators under MRSS have clearly lightened superiority over SRS. The efficiency comparisons, Monte-Carlo simulation, and empirical study through COVID-19 pandemic data have also shown supremacy under MRSS.

Keywords: Subsidiary variable; Predictive model; Median ranked set sampling; Mean square error; 62D05 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13226-023-00470-7

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