Handbook of Measurement Error Models  book cover
1st Edition

Handbook of Measurement Error Models

ISBN 9781138106406
Published October 18, 2021 by Chapman and Hall/CRC
592 Pages 33 B/W Illustrations

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Book 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.


  • 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

Table of Contents

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 data

Jeffrey 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

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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).