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Measurement Error

Models, Methods, and Applications

Measurement Error by John P. Buonaccorsi
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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
The additive Berkson model and consequences
The additive measurement error model
The behavior of naive analyses
Correcting for additive measurement error
Residual analysis
Mathematical developments

Multiple Linear Regression
Model for true values
Models and bias in naive estimators
Correcting for measurement error
Weighted and other estimators
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
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

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
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

CRC Press; March 2010
465 pages; ISBN 9781420066586
Read online, or download in secure PDF format
Title: Measurement Error
Author: John P. Buonaccorsi
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