Estimating the Population Mean in Stratified Median ranked Set Sampling Using Combined and Separate Regression with the Presence of Outliers

Section: Article
Issue
Vol. 22 No. 2 (2025): Volume 22 Issue 2
Published
Nov 30, 2025
Pages
39-51

Abstract

This research aims to demonstrate the high efficiency and accuracy in estimating the limited population mean through the estimates of the separate and combined stratified regression line based on the method of median ranked set sampling to choose a sample that is more representative of the community. With the problem of heterogeneity in the data and containing extreme values ​​(outliers), it is recommended to use stratification of the community and draw samples using the method of sampling the middle ordered from these layers, which is known as the stratified median ranked set sampling ( ), which is one of the modified ranked set  sampling methods (RSS), where the mean square error (MSE) of the population mean estimator obtained in this way was compared with the MSE value of the population mean estimator obtained through regression estimates using the robust variance and covariance matrix (Minimum Covariance Determinant (MCD), Minimum Volume Ellipsoid (MVE)) to calculate the averages and using robust methods (Huber M, Huber MM, Least Median of Squares (LMS), Least Trimmed Squares (LTS)) to estimate the regression parameter. The simulation results show that the proposed estimator outperforms the robust estimators in most cases because it obtains the lowest values of the mean square error.

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Authors

Ayman M. AL-Hadidi

Department of Statistics and Informatics, College of Computer Science and Mathematics University of Mosul, Mosul, Iraq

Rikan A. Ahmed

Department of Statistics and Informatics, College of Computer Science and Mathematics, University of Mosul, Mosul, Iraq

Identifiers

https://doi.org/10.33899/iqjoss.v22i2.54071
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How to Cite

AL-Hadidi, A. M. ., & Ahmed, R. A. . (2025). Estimating the Population Mean in Stratified Median ranked Set Sampling Using Combined and Separate Regression with the Presence of Outliers. IRAQI JOURNAL OF STATISTICAL SCIENCES, 22(2), 39–51. https://doi.org/10.33899/iqjoss.v22i2.54071