Addressing selection bias and measurement error in COVID-19 case count data using auxiliary information-Reference-Cited by-同舟云学术

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Addressing selection bias and measurement error in COVID-19 case count data using auxiliary information

Published:2023-12-01 Issue:4 Volume:17 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Dempsey Walter¹

Affiliation:

1. Department of Biostatistics, University of Michigan

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference57 articles.

1. Elliott, M. R. and Valliant, R. (2017). Inference for nonprobability samples. Statist. Sci. 32 249–264.

2. CHEN, Y., LI, P. and WU, C. (2020). Doubly robust inference with nonprobability survey samples. J. Amer. Statist. Assoc. 115 2011–2021.

3. VALLIANT, R. and DEVER, J. A. (2011). Estimating propensity adjustments for volunteer web surveys. Sociol. Methods Res. 40 105–137.

4. COX, D. R. and HINKLEY, D. V. (1974). Theoretical Statistics. CRC Press, London.

5. DONG, E., DU, H. and GARDNER, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 20 533–534.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploring the Big Data Paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS);Science Advances;2024-05-31

2. Assessing COVID-19 Prevalence in Austria with Infection Surveys and Case Count Data as Auxiliary Information;Journal of the American Statistical Association;2024-03-08

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