A new improved generalized class of estimators for population distribution function using auxiliary variable under simple random sampling

This article aims to suggest a new improved generalized class of estimators for finite population distribution function of the study and the auxiliary variables as well as mean of the usual auxiliary variable under simple random sampling. The numerical expressions for the bias and mean squared error...

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Bibliographic Details
Published in:Scientific reports 2023-04, Vol.13 (1), p.5415-5415, Article 5415
Main Authors: Ahmad, Sohaib, Ullah, Kalim, Zahid, Erum, Shabbir, Javid, Aamir, Muhammad, Alshanbari, Huda M., El-Bagoury, Abd Al-Aziz Hosni
Format: Article
Language:English
Subjects:
639/166
639/705
639/705/1041
639/705/1042
639/705/1046
639/705/531
Bias
Humanities and Social Sciences
multidisciplinary
Population distribution
Science
Science (multidisciplinary)
Statistical sampling
Variables
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Summary:This article aims to suggest a new improved generalized class of estimators for finite population distribution function of the study and the auxiliary variables as well as mean of the usual auxiliary variable under simple random sampling. The numerical expressions for the bias and mean squared error (MSE) are derived up to first degree of approximation. From our generalized class of estimators, we obtained two improved estimators. The gain in second proposed estimator is more as compared to first estimator. Three real data sets and a simulation are accompanied to measure the performances of our generalized class of estimators. The MSE of our proposed estimators is minimum and consequently percentage relative efficiency is higher as compared to their existing counterparts. From the numerical outcomes it has been shown that the proposed estimators perform well as compared to all considered estimators in this study.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-30150-9