Estimation of mask effectiveness perception for small domains using multiple data sources
| Autor: | Sen, Aditi, Lahiri, Partha |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Statistics in Transition new series, vol. 23, 2022, 1, pages: 1 to 20 |
| Druh dokumentu: | Working Paper |
| Popis: | All pandemics are local; so learning about 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 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 propose a Jackknife method to estimate variance of our proposed estimator. From our data analysis. it is evident that our proposed synthetic method outperforms direct survey-weighted estimator with respect to commonly used evaluation measures. Comment: 35 pages, 7 figures |
| Databáze: | arXiv |
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