| Contents Notes |
Part 1. Introduction1. Measurement error models -- A brief account of past developments and modern advancementsGrace Y. Yi/Jeffrey S Buzas2. The impact of unacknowledged measurement errorPaul GustafsonPart 2. Identifiability and Estimation3. Identifiability in measurement errorLiqun Wang4. Partial learning of misclassification parametersPaul Gustafson5. Using instrumental variables to estimate models with mismeasured regressorsArthur LewbelPart 3. General Methodology6. Likelihood Methods for Measurement Error and MisclassificationGrace Y. Yi7. Regression calibration for covariate measurement errorPamela A. Shaw8. Conditional and corrected score methodsDavid M. Zucker 9. Semiparametric methods for measurement error and misclassificationYanyuan MaPart 4. Nonparametric Inference10. Deconvolution kernel density estimationAurore Delaigle11. Nonparametric deconvolution by Fourier transformation and other related approachesYicheng Kang/Peihua Qiu12. Deconvolution with unknown error distributionAurore Delaigle, Ingrid Van Keilegom13. Nonparametric inference methods for Berkson errorsWeixing Song14. Nonparametric Measurement Errors Models for RegressionTatiyana Apanasovich/Hua LiangPart 5. Applications 15. Covariate measurement error in survival dataJeffrey S. Buzas16. Mixed effects models with measurement errors in time-dependent covariatesLang Wu/Wei Liu/Hongbin Zhang17. Estimation in mixed-effects models with measurement errorLiqun Wang18. Measurement error in dynamic models John P. Buonaccorsi19. Spatial exposure measurement error in environmental epidemiologyHoward H. Chang, Joshua P. KellerPart 6. Other features20. Measurement error as a missing data problemRuth H. Keogh, Jonathan W. Bartlett21. Measurement error in causal inferenceLinda Valeri22. Measurement error and misclassification in meta-analysis Annamaria GuoloPart 7. Bayesian Analysis23. Bayesian adjustment for misclassificationJames D. Stamey and John W. Seaman Jr. 24. Bayesian approaches for handling covariate measurement errorSamiran Sinha. 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. |
