Handbook of Measurement Error Models
1st Edition
Published on 17. October 2021
592 pages
978-1-351-58859-1 (ISBN)
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
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
Reviews / Votes
"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
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
33 Line drawings, black and white; 33 Illustrations, black and white
ISBN-13
978-1-351-58859-1 (9781351588591)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Grace Y. Yi | Aurore Delaigle | Paul Gustafson
Handbook of Measurement Error Models
Book
01/2024
1st Edition
Chapman & Hall/CRC
€116.20
Shipment within 10-20 days
Grace Y. Yi | Aurore Delaigle | Paul Gustafson
Handbook of Measurement Error Models
Book
10/2021
1st Edition
CRC Press
€313.70
Shipment within 10-20 days
Persons
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).
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).
Content
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
