Measurement Error: Models, Methods, and Applications (Chapman & Hall/CRC Interdisciplinary Statistics)
Language: English
Published by Chapman and Hall/CRC, 2023
Series: Book 17 of 45 - Chapman & Hall/CRC Interdisciplinary Statistics
- Softcover
- New
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- Title
- Measurement Error: Models, Methods, and Applications (Chapman & Hall/CRC Interdisciplinary Statistics)
- Author
- Buonaccorsi, John P.
- Publisher
- Chapman and Hall/CRC
- Publication year
- 2023
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1032477687
- ISBN 13
- 9781032477688
- Series
- Book 17 of 45: Chapman & Hall/CRC Interdisciplinary Statistics
Over the last 20 years, comprehensive strategies for treating measurement error in complex models and accounting for the use of extra data to estimate measurement error parameters have emerged. Focusing on both established and novel approaches, Measurement Error: Models, Methods, and Applications provides an overview of the main techniques and illustrates their application in various models. It describes the impacts of measurement errors on naive analyses that ignore them and presents ways to correct for them across a variety of statistical models, from simple one-sample problems to regression models to more complex mixed and time series models.
The book covers correction methods based on known measurement error parameters, replication, internal or external validation data, and, for some models, instrumental variables. It emphasizes the use of several relatively simple methods, moment corrections, regression calibration, simulation extrapolation (SIMEX), modified estimating equation methods, and likelihood techniques. The author uses SAS-IML and Stata to implement many of the techniques in the examples.
Accessible to a broad audience, this book explains how to model measurement error, the effects of ignoring it, and how to correct for it. More applied than most books on measurement error, it describes basic models and methods, their uses in a range of application areas, and the associated terminology.
"Synopsis" may belong to another edition of this title.
About the Author
John P. Buonaccorsi is a professor in the Department of Mathematics and Statistics at the University of Massachusetts, Amherst.
"About the title" may belong to another edition of this title.
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