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Measurement Error als Buch (gebunden)

Measurement Error

Models, Methods, and Applications. 94 Tables, black and white; 30 Illustrations, black and white. Sprache:…
Buch (gebunden)
Measurement Error provides an understanding of measurement error, the effects of ignoring it, and how to correct for these effects. The book focuses on the models and methods involved and demonstrates how they can be implemented in practice. Keeping … weiterlesen
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Measurement Error als Buch (gebunden)


Titel: Measurement Error
Autor/en: John P. (University of Massachusetts, Amherst, Massachusetts, USA) Buonaccorsi

ISBN: 1420066560
EAN: 9781420066562
Models, Methods, and Applications.
94 Tables, black and white; 30 Illustrations, black and white.
Sprache: Englisch.
Taylor & Francis Ltd

2. März 2010 - gebunden - 464 Seiten


Measurement Error provides an understanding of measurement error, the effects of ignoring it, and how to correct for these effects. The book focuses on the models and methods involved and demonstrates how they can be implemented in practice. Keeping theory to a minimum with an appendix of theoretical background, it presents numerous examples from biostatistics and epidemiology as well as ecology and the social sciences. The author implements these examples using available Stata routines and his own SAS programs. Topics covered include misclassification in estimation, measurement error in inference, predictors, and time series.


Introduction What is measurement error?
Some examples
The main ingredients
Some terminology A look ahead

Misclassification in Estimating a Proportion
Motivating examples A model for the true values
Misclassification models and naive analyses
Correcting for misclassification Finite populations
Multiple measures with no direct validation
The multinomial case Mathematical developments

Misclassification in Two-Way Tables
Models for true values
Misclassification models and naive estimators
Behavior of naive analyses Correcting using external validation data Correcting using internal validation data General two-way tables Mathematical developments

Simple Linear Regression Introduction
The additive Berkson model and consequences
The additive measurement error model
The behavior of naive analyses
Correcting for additive measurement error Examples Residual analysis
Prediction Mathematical developments

Multiple Linear Regression Introduction
Model for true values
Models and bias in naive estimators
Correcting for measurement error Weighted and other estimators
Examples Instrumental variables Mathematical developments

Measurement Error in Regression: A General Overview
Models for true values
Analyses without measurement error
Measurement error models Extra data Assessing bias in naive estimators Assessing bias using induced models Assessing bias via estimating equations Moment based and direct bias corrections
Regression calibration and quasi-likelihood methods
Simulation extrapolation (SIMEX)
Correcting using likelihood methods Modified estimating equation approaches Correcting for misclassification
Overview on use of validation data Bootstrapping Mathematical developments

Binary Regression
Additive measurement error Using validation data Misclassification of predictors

Linear Models with Nonadditive Error
Quadratic regression First-order models with interaction General nonlinear functions of the predictors Linear measurement error with validation data Misclassification of a categorical predictor Miscellaneous

Nonlinear Regression
Poisson regression: Cigarettes and cancer rates
General nonlinear models

Error in the Response
Additive error in a single sample Linear measurement error in the one-way setting Measurement error in the response in linear models

Mixed/Longitudinal Models
Introduction, overview, and some examples
Berkson error in designed repeated measures Additive error in the linear mixed model

Time Series Introduction
Random walk/population viability models Linear autoregressive models

Background Material
Notation for vectors, covariance matrices, etc.
Double expectations
Approximate Wald inferences
The delta-method: approximate moments of nonlinear functions Fieller's method for ratios


Author Index

Subject Index


John P. Buonaccorsi is a professor in the Department of Mathematics and Statistics at the University of Massachusetts, Amherst.


The author has written a praiseworthy summary of available results on measurement errors in a wide variety of statistical models. The author also covers results described in very recent papers, which have not been previously published in any other book. ... The book brings a big help for theoretical researchers as well as applied statisticians who deal with data contaminated by measurement errors. The author demonstrates a very deep understanding for the theory and does not hesitate to discuss many specific theoretical problems. He succeeds very well in illustrating the methods on real examples and explaining the ideas to applied statisticians. Although not primarily intended for biostatisticians, I would say the book is suitable exactly for epidemiological and biostatistical applications. ... very clearly and systematically organized. ... the book offers an excellent and remarkable overview of available methods for incorporating measurement errors to statistical analysis. -Jan Kalina, ISCB News, 52, December 2011 ... we think Buonaccorsi's book would be a great textbook ... The book also contains many interesting data examples, which are useful for those concerned with applications. Overall, the book is also a good reference resource ... We would recommend this book to people who are interested in statistical methods for measurement error. -C.Y. Wang and X. Song, The American Statistician, August 2011 This book is a successful attempt to collect, organize and present the literature over the newly developed and earlier existing topics of measurement error models in one place. ...The material that is presented in chapters [11 and 12] is, to my knowledge, not available in any other book on this area. ... This book should be of immense help to those who are interested in the theoretical as well as applied aspects of measurement error models. ... Some topics in the book may be used to teach advanced graduate courses. ... The book is overall well written, presents updated developments in the area of measurement error models and is an excellent guide to applications. I am sure that it will stimulate researchers in and newcomers to this area. -Journal of the Royal Statistical Society, Series A, April 2011 There are plenty of illustrations and worked examples throughout ... The book is very readable and clearly demonstrates the importance of recognizing measurement error, which is often ignored as a bit of a nuisance to be swept under the carpet. Together with easily accessible software, in the future, the problem is likely to be more commonly addressed and dealt with properly. -International Statistical Review (2010), 78, 3

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