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.
General
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