This work describes the impacts of measurement errors on naive analyses that ignore them and presents ways to correct for them across a variety of statistical models, from simple one-sample problems and misclassification in categorical models to regression models to more complex mixed and time series models. It covers correction methods based on known measurement error parameters, replication, internal or external validation data, and instrumental variables. The author includes examples using real-world data from ...
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This work describes the impacts of measurement errors on naive analyses that ignore them and presents ways to correct for them across a variety of statistical models, from simple one-sample problems and misclassification in categorical models to regression models to more complex mixed and time series models. It covers correction methods based on known measurement error parameters, replication, internal or external validation data, and instrumental variables. The author includes examples using real-world data from epidemiology, ecology, and other disciplines and employs SAS-IML and Stata to implement many of the techniques in the examples.
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