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Measurement Error and Misclassification in Statistics and Epidemiology

Gustafson, Paul·2003·Chapman & Hall/CRC eBooks

Abstract

Mismeasurement of explanatory variables is a common hazard when using statistical modeling techniques, and particularly so in fields such as biostatistics and epidemiology where perceived risk factors cannot always be measured accurately. With this perspective and a focus on both continuous and categorical variables, Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments examines the consequences and Bayesian remedies in those cases where the explanatory variable cannot be measured with precision. The author explores both measurement error in continuous variables and misclassification in discrete variables, and shows how Bayesian methods might be used to allow for mismeasurement. A broad range of topics, from basic research to more complex concepts such as "wrong-model" fitting, make this a useful research work for practitioners, students and researchers in biostatistics and epidemiology."

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Metadata

Authors
Gustafson, P.
Venue
Chapman & Hall/CRC eBooks
Published
2003-09-25
OpenAlex
W4242238911
Cited by
245
References
0
FWCI
2.04
Type
book
Language
en
Open access
closed

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CoChat. (2026). Citation check: Measurement Error and Misclassification in Statistics and Epidemiology. https://app.cochat.ai/papers/10.1201/9780203502761
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Gustafson, P. (2003). Measurement Error and Misclassification in Statistics and Epidemiology. Chapman & Hall/CRC eBooks. https://doi.org/10.1201/9780203502761

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.