Handbook of Measurement Error Models (eBook, ePUB)

Redaktion: Yi, Grace Y.; Gustafson, Paul; Delaigle, Aurore

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 592
• Erscheinungstermin: 17. Oktober 2021
• Englisch
• ISBN-13: 9781351588584
• Artikelnr.: 62649406

Autorenporträt

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).

Inhaltsangabe

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