Measurement Error

Models, Methods, and Applications

John P. Buonaccorsi, Byron J.T. Morgan (University of Kent, UK), Niels Keiding (University of Copenhagen, Denmark) , Peter Van der Heijden (Utrecht University, The Netherlands)

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English

CRC Press Inc
02 March 2010

Research methods: general; Probability & statistics

Series: Chapman & Hall/CRC Interdisciplinary Statistics

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.

By: John P. Buonaccorsi
Series edited by: Byron J.T. Morgan (University of Kent UK), Niels Keiding (University of Copenhagen, Denmark), Peter Van der Heijden (Utrecht University, The Netherlands), Terry Speed (University of California, Berkeley, USA)
Imprint: CRC Press Inc
Country of Publication: United States
Volume: v. 26
Dimensions: Height: 234mm, Width: 156mm, Spine: 28mm
Weight: 1.020kg
ISBN: 9781420066562
ISBN 10: 1420066560
Series: Chapman & Hall/CRC Interdisciplinary Statistics
Pages: 464
Publication Date: 02 March 2010
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

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

Reviews for Measurement Error: Models, Methods, and Applications

! 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