- Autor
- Sen Aditi (The Applied Mathematics & Statistics, and Scientific Computation, University of Maryland, USA), Lahiri Partha (University of Maryland)
- Tytuł
- Estimation of Mask Effectiveness Perception for Small Domains Using Multiple Data Sources
- Źródło
- Statistics in Transition, 2022, vol. 23, nr 1, s. 1-20, tab., wykr., bibliogr. 13 poz.
- Słowa kluczowe
- Estymacja, Badania ankietowe, COVID-19, Pandemia, Metody statystyczne
Estimation, Questionnaire survey, COVID-19, Pandemic, Statistical methods - Uwagi
- summ.
- Abstrakt
- 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. (original abstract)
- Dostępne w
- Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka SGH im. Profesora Andrzeja Grodka
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach - Pełny tekst
- Pokaż
- Bibliografia
- Angrisani, M., A. Kapteyn, E. Meijer, and H.-W. Saw, (2019). Sampling and weighting the Understanding America Study, Working Paper, No. 2019-004, Los Angeles, CA: University of Southern California, Center for Economic and Social Research.
- Census Bureau, (2010). Census Regions and Divisions of the United States.
- Ghosh, M., (2020). Small area estimation: Its evolution in five decades. Statistics in Transition New Series, Special Issue on Statistical Data Integration, pp. 1-22.
- Hansen, M., W. Hurwitz, and W. Madow, (1953). Sample Survey Methods and Theory, Vol. 1. Wiley-Interscience.
- Lahiri, P. and S. Pramanik, (2019). Estimation of average design-based mean squared error of synthetic small area estimators, Austrian Journal of Statistics, 48, pp. 43-57.
- Lumley, T., (2004). Analysis of complex survey samples, Journal of Statistical Software, 9(1), pp. 1-19, R package verson 2.2.
- Lumley, T., (2010). Complex Surveys: A Guide to Analysis Using R: A Guide to Analysis Using R. John Wiley and Sons.
- Lumley, T., (2020). Survey: analysis of complex survey samples, R package version 4.0.
- Marker, D., (1995). Small Area Estimation: A Bayesian Perspective. Phd thesis, University of Michigan, Ann Arbor, MI.
- Marker, D., (1999). Organization of small area estimators using a generalized linear regression framework, Journal of Offcial Statistics, 15, pp. 1-24.
- Rao, J. N. K., I. Molina, (2015). Small Area Estimation, 2nd Edition, Wiley.
- Stasny, E., P. Goel, and D. Rumsey, (1991). County estimates of wheat production, Survey Methodology, 17 (2), pp. 211-225.
- U.S. Census Bureau, (2020). Census Bureau Population Estimates. Wikipedia, (2020). Political party strength in U.S. states.
- Cytowane przez
- ISSN
- 1234-7655
- Język
- eng
- URI / DOI
- http://dx.doi.org/10.21307/stattrans-2022-001