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

DOI10.1214/23-AOAS1744arXiv2005.10425MaRDI QIDQ6138611

Walter Dempsey

Publication date: 16 January 2024

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.10425

zbMATH Keywords

measurement errorselection biasinverse-probability weightingdoubly robust estimationCOVID-19

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Cites Work

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• BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic
• Statistical paradises and paradoxes in big data. I: Law of large populations, big data paradox, and the 2016 US presidential election
• Model-assisted survey estimation with modern prediction techniques
• Inference for nonprobability samples
• Forecasting seasonal influenza with a state-space SIR model
• Doubly Robust Inference With Nonprobability Survey Samples
• A Generalization of Sampling Without Replacement From a Finite Universe

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