Estimation of mask effectiveness perception for small domains using multiple data sources

• Authors: Aditi Sen , Partha Lahiri

Abstracts

EN

Understanding the impacts of pandemics on public health and related societal issues at granular levels is of great interest. COVID-19 is affecting everyone in the globe and mask wearing is one of the few precautions against it. To quantify people's perception of mask effectiveness and to prevent the spread of COVID-19 for small areas, we use Understanding America Study's (UAS) survey data on COVID-19 as our primary data source. Our data analysis shows that direct survey-weighted estimates for small areas could be highly unreliable. In this paper, we develop a synthetic estimation method to estimate proportions of perceived mask effectiveness for small areas using a logistic model that combines information from multiple data sources. We select our working model using an extensive data analysis facilitated by a new variable selection criterion for survey data and benchmarking ratios. We suggest a jackknife method to estimate the variance of our estimator. From our data analysis, it is evident that our proposed synthetic method outperforms the direct survey-weighted estimator with respect to commonly used evaluation measures.

Keywords

EN

cross-validation; jackknife; survey data; synthetic estimation

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2022
• Volume: 23
• Issue: 1
• Pages: 1-20

Dates

published

2022

Contributors

author

Aditi Sen

• Applied Mathematics & Statistics, and Scientific Computation

author

Partha Lahiri

• The Joint Program in Survey Methodology & Professor, Department of Mathematics, University of Maryland, USA

References

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Identifiers

DOI

10.21307/stattrans-2022-001

Biblioteka Nauki

2028543