Main description:

Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention.

The Handbook of Measurement Error Models provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike.

Features:

Provides an account of past development and modern advancement concerning measurement error problems

Highlights the challenges induced by error-contaminated data

Introduces off-the-shelf methods for mitigating deleterious impacts of measurement error

Describes state-of-the-art strategies for conducting in-depth research

Contents:

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