Measurement Error : Models, Methods, and Applications

John P. Buonaccorsi

ISBN 10: 1420066560 / ISBN 13: 9781420066562

Published by Chapman And Hall/CRC, 2010

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9781420066562

Bibliographic Details

Title: Measurement Error : Models, Methods, and ...

Publisher: Chapman And Hall/CRC

Publication Date: 2010

Binding: Buch

Condition: Neu

About this title

Synopsis:

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.

About the Author:

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