ArticleConcurrency and computation : practice & experience2022
Modified unbiased estimators for population variance: An application for COVID-19 deaths in Russia.
Article in Concurrency and computation : practice & experience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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3 citing papers in PubMed.
- Difference-Cum-Exponential-type estimators for estimation of finite population mean in survey sampling.PloS one · 2025Article
- Interval price prediction of livestock product based on fuzzy mathematics and improved LSTM.PloS one · 2025Article
- Modified unbiased estimators for population variance: An application for COVID-19 deaths in Russia.Concurrency and computation : practice & experience · 2022Article
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Abstract
The article deal with the class unbiased forms of the variance estimators using Hartley-Ross type in the simple random sampling. The mean squared error (since it is an unbiased estimator, variance is calculated) of the suggested, up to the first order of approximation, is derived. The proposed estimator using COVID-19 data in Russia has been proven to be more efficient than the considered estimators under the conditions. Thus, it allows us to see the variance estimator that best predicts the change according to federal regions in Russia's number of COVID-19 deaths. This study also provides an unbiased family of estimators that are more efficient than existing estimators, which estimate the variance of the total death number of COVID-19 based on the daily new cases number. The most important difference of this article from other studies in the literature is that it is the first study to examine the variance of the COVID-19 cumulative mortality value in terms of variance estimation using the simple random sampling method with the auxiliary variable.
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