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Transformed ratio type estimators under Adaptive Cluster Sampling: An application to COVID-19 |
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PP: 63-70 |
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doi:10.18576/jsapl/090201
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Author(s) |
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Rajesh Singh,
Rohan Mishra,
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Abstract |
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| The initial spread of COVID-19 is highly clustered or clumped. In such a case conventional sampling designs viz., Simple random sampling without replacement, Stratified random sampling or other non-adaptive designs cannot be used to estimate the average cases of COVID-19 as the sample drawn will not be a representative one. In this article, we have proposed four transformed ratio type estimators in Adaptive cluster sampling (ACS) to estimate the average of new COVID-19 cases. The expressions of bias and mean squared error (MSE) of the proposed estimators are derived up to first order of approximation and presented. The performance of the proposed estimators have been analyzed through a simulation study, on COVID-19 cases in the Indian state of Goa. The proposed estimators perform better than all the estimators presented in this article.
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