1st Edition
Handbook of Measurement Error Models
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
Part 1. Introduction
1. Measurement error models - A brief account of past developments and modern advancements
Grace Y. Yi/Jeffrey S Buzas
2. The impact of unacknowledged measurement error
Paul Gustafson
Part 2. Identifiability and Estimation
3. Identifiability in measurement error
Liqun Wang
4. Partial learning of misclassification parameters
Paul Gustafson
5. Using instrumental variables to estimate models with mismeasured regressors
Arthur Lewbel
Part 3. General Methodology
6. Likelihood Methods for Measurement Error and Misclassification
Grace Y. Yi
7. Regression calibration for covariate measurement error
Pamela A. Shaw
8. Conditional and corrected score methods
David M. Zucker
9. Semiparametric methods for measurement error and misclassification
Yanyuan Ma
Part 4. Nonparametric Inference
10. Deconvolution kernel density estimation
Aurore Delaigle
11. Nonparametric deconvolution by Fourier transformation and other related approaches
Yicheng Kang/Peihua Qiu
12. Deconvolution with unknown error distribution
Aurore Delaigle, Ingrid Van Keilegom
13. Nonparametric inference methods for Berkson errors
Weixing Song
14. Nonparametric Measurement Errors Models for Regression
Tatiyana Apanasovich/Hua Liang
Part 5. Applications
15. Covariate measurement error in survival dataJeffrey S. Buzas
16. Mixed effects models with measurement errors in time-dependent covariates
Lang Wu/Wei Liu/Hongbin Zhang
17. Estimation in mixed-effects models with measurement error
Liqun Wang
18. Measurement error in dynamic models
John P. Buonaccorsi
19. Spatial exposure measurement error in environmental epidemiology
Howard H. Chang, Joshua P. Keller
Part 6. Other features
20. Measurement error as a missing data problem
Ruth H. Keogh, Jonathan W. Bartlett
21. Measurement error in causal inference
Linda Valeri
22. Measurement error and misclassification in meta-analysis
Annamaria Guolo
Part 7. Bayesian Analysis
23. Bayesian adjustment for misclassification
James D. Stamey and John W. Seaman Jr.
24. Bayesian approaches for handling covariate measurement error
Samiran Sinha
Biography
Grace Y. Yi is Professor of Statistics at the University of Western Ontario where she holds a Tier I Canada Research Chair in Data Science. She is a Fellow of the Institute of Mathematical Statistics (IMS), a Fellow of the American Statistical Association (ASA), and an Elected Member of the International Statistical Institute (ISI). She authored the monograph Statistical Analysis with Measurement Error or Misclassification (2017, Springer).
Aurore Delaigle is Professor at the School of Mathematics and Statistics at the University of Melbourne. She is a Fellow of the Australian Academy of Science, a Fellow of the Institute of Mathematical Statistics (IMS), a Fellow of the American Statistical Association (ASA), and an Elected Member of the International Statistical Institute (ISI). She is a past recipient of the George W. Snedecor Award from the Committee of Presidents of Statistical Societies (COPSS) and of the Moran Medal from the Australian Academy of Science.
Paul Gustafson is Professor and Head of the Department of Statistics at the University of British Columbia. He is a Fellow of the American Statistical Association, the 2020 Gold Medalist of the Statistical Society of Canada, and the author of the monograph Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments (2004, Chapman and Hall, CRC Press).
"This handbook provides detailed and comprehensive developments and methods for meta-analysis. Its insights and clear explanations make readers easily learn fundamental and advanced approaches to meta-analysis. This book is a valuable reference to develop new methods in meta-analysis and relevant materials provide motivating extensions in the future research."
- Biometrics"Written by rigorous mathematical language, the papers in the book can be useful to professional statisticians and graduate students specializing in advanced regression modeling and analysis of data with measurement errors."
- Stan Lipovetsky in Technometrics, April 2023
