Efficient class of ratio cum median estimators for estimating the population median

In estimation theory, the use of auxiliary information significantly improves precision while estimating population parameters. In this paper, an efficient class of ratio cum median estimators of the population median is suggested using simple random sampling without replacement. The expressions for...

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Main Authors: Mir Subzar, Showkat Ahmad Lone, Emmanuel J. Ekpenyong, Abdul Salam, Muhammad Aslam, T. A. Raja, Salmeh A. Almutlak
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2023-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9910762/?tool=EBI
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author Mir Subzar
Showkat Ahmad Lone
Emmanuel J. Ekpenyong
Abdul Salam
Muhammad Aslam
T. A. Raja
Salmeh A. Almutlak
author_facet Mir Subzar
Showkat Ahmad Lone
Emmanuel J. Ekpenyong
Abdul Salam
Muhammad Aslam
T. A. Raja
Salmeh A. Almutlak
author_sort Mir Subzar
collection DOAJ
description In estimation theory, the use of auxiliary information significantly improves precision while estimating population parameters. In this paper, an efficient class of ratio cum median estimators of the population median is suggested using simple random sampling without replacement. The expressions for bias and mean square error of the proposed class are derived theoretically. The condition for the asymptotic optimum estimator is obtained with its bias and mean square error expressions. Under certain realistic conditions, the asymptotic optimum estimator is more proficient, based on analytical and numerical comparisons with some existing estimators that are members of the suggested class of estimators. The superiority of the proposed ratio cum median estimators is shown through real data applications. Such a new proposed estimator will be useful in the future for data analysis and making decisions.
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spelling doaj.art-16dc5402abe94552a87d223c4346250d2023-02-12T05:31:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01182Efficient class of ratio cum median estimators for estimating the population medianMir SubzarShowkat Ahmad LoneEmmanuel J. EkpenyongAbdul SalamMuhammad AslamT. A. RajaSalmeh A. AlmutlakIn estimation theory, the use of auxiliary information significantly improves precision while estimating population parameters. In this paper, an efficient class of ratio cum median estimators of the population median is suggested using simple random sampling without replacement. The expressions for bias and mean square error of the proposed class are derived theoretically. The condition for the asymptotic optimum estimator is obtained with its bias and mean square error expressions. Under certain realistic conditions, the asymptotic optimum estimator is more proficient, based on analytical and numerical comparisons with some existing estimators that are members of the suggested class of estimators. The superiority of the proposed ratio cum median estimators is shown through real data applications. Such a new proposed estimator will be useful in the future for data analysis and making decisions.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9910762/?tool=EBI
spellingShingle Mir Subzar
Showkat Ahmad Lone
Emmanuel J. Ekpenyong
Abdul Salam
Muhammad Aslam
T. A. Raja
Salmeh A. Almutlak
Efficient class of ratio cum median estimators for estimating the population median
PLoS ONE
title Efficient class of ratio cum median estimators for estimating the population median
title_full Efficient class of ratio cum median estimators for estimating the population median
title_fullStr Efficient class of ratio cum median estimators for estimating the population median
title_full_unstemmed Efficient class of ratio cum median estimators for estimating the population median
title_short Efficient class of ratio cum median estimators for estimating the population median
title_sort efficient class of ratio cum median estimators for estimating the population median
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9910762/?tool=EBI
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