Measurement Error Modeling: Advances and Applications
Organiser
Participants
Category: International Association of Survey Statisticians (IASS)
Abstract
With an increase in data availability, researchers have been challenged to account for diverse error sources in model-based estimation methods. Without properly accounting for measurement error, analyses may lead to biased estimates. Motivated by economic and environmental problems, this session consists of four presentations on recent advances and applications in model-based estimation using covariates measured with error.
Submissions
- A comparison of predictors for a measurement error Fay Herriot model
- Accounting for dose uncertainty in dose-response curve estimation using hierarchical Bayes models
- Robustness against measurement errors in linear regression analysis
- Spatial modeling of infectious diseases with covariates measurement error