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Measurement Error
Models, Methods, and Applications
Taschenbuch von John P. Buonaccorsi
Sprache: Englisch

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Beschreibung
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.

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.

Inhaltsverzeichnis
Introduction. Misclassification in Estimating a Proportion. Misclassification in Two-Way Tables. Simple Linear Regression. Multiple Linear Regression. Measurement Error in Regression: A General Overview. Binary Regression. Linear Models with Nonadditive Error. Nonlinear Regression. Error in the Response. Mixed/Longitudinal Models. Time Series. Background Material. References. Indices.
Details
Medium: Taschenbuch
ISBN-13: 9781032477688
ISBN-10: 1032477687
Sprache: Englisch
Autor: Buonaccorsi, John P.
Hersteller: Taylor & Francis
Chapman and Hall/CRC
Verantwortliche Person für die EU: preigu, Ansas Meyer, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de
Abbildungen: 30 SW-Abb.
Maße: 25 x 156 x 234 mm
Von/Mit: John P. Buonaccorsi
Gewicht: 0,701 kg
Artikel-ID: 131007672
Inhaltsverzeichnis
Introduction. Misclassification in Estimating a Proportion. Misclassification in Two-Way Tables. Simple Linear Regression. Multiple Linear Regression. Measurement Error in Regression: A General Overview. Binary Regression. Linear Models with Nonadditive Error. Nonlinear Regression. Error in the Response. Mixed/Longitudinal Models. Time Series. Background Material. References. Indices.
Details
Medium: Taschenbuch
ISBN-13: 9781032477688
ISBN-10: 1032477687
Sprache: Englisch
Autor: Buonaccorsi, John P.
Hersteller: Taylor & Francis
Chapman and Hall/CRC
Verantwortliche Person für die EU: preigu, Ansas Meyer, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de
Abbildungen: 30 SW-Abb.
Maße: 25 x 156 x 234 mm
Von/Mit: John P. Buonaccorsi
Gewicht: 0,701 kg
Artikel-ID: 131007672
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