Measurement Error - Buonaccorsi, John P.
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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.…mehr

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