ArticleHeliyon2023
A simulation study: An enhanced generalized class of estimators for estimation of population proportion using twofold auxiliary attribute.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The article introduces a novel class of estimators designed for estimating finite population proportions. These estimators utilize dual auxiliary attributes and are applicable under simple random sampling. The proposed class of estimators includes various members with distinct characteristics. The article provides numerical terminologies for the bias and MSE of the estimators, acquire up to first order of approximation. Four actual data sets are used. Additionally, a simulation study is accompanied to perceive the presentations of estimators. The MSE criterion is used to assess how well the proposed estimator performed as likened to the preliminary estimators. The simulation analysis revealed that, in contrast to other examined estimators, the suggested class of estimators provided better results. The empirical investigation offers evidence to substantiate the findings of the argument. Theoretical research also displays that the suggested class of estimators outperforms its competitors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.