1. Measurement Error models - A brief account of past developments and modern advancements.
2. The impact of unacknowledged measurement error.
3. Identifiability in measurement error.
4. Partial learning of misclassification parameters.
5. Using instrumental variables to estimate models with mismeasured regressors.
6. Likelihood Methods for Measurement Error and Misclassification.
7. Regression calibration for covariate measurement error.
8. Conditional and corrected score methods.
9. Semiparametric methods for measurement error and misclassification.
10. Deconvolution kernel density estimation.
11. Nonparametric deconvolution by Fourier transformation and other related approaches.
12. Deconvolution with unknown error distribution.
13. Nonparametric inference methods for Berkson errors.
14. Nonparametric Measurement Errors Models for Regression.
15. Covariate measurement error in survival data.
16. Mixed effects models with measurement errors in time-dependent covariates.
17. Estimation in mixed-effects models with measurement error.
18. Measurement error in dynamic models .
19. Spatial exposure measurement error in environmental epidemiology.
20. Measurement error as a missing data problem.
21. Measurement error in causal inference.
23. Bayesian adjustment for misclassification.
24. Bayesian approaches for handling covariate measurement error