Latent-model robustness in structural measurement error models
Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, U.S.A. xhuang{at}ncsu.edu, stefanski{at}ncsu.edu, davidian{at}ncsu.edu
We present methods for diagnosing the effects of model misspecification of the true-predictor distribution in structural measurement error models. We first formulate latent-model robustness theoretically. Then we provide practical techniques for examining the adequacy of an assumed latent predictor model. The methods are illustrated via analytical examples, application to simulated data and with data from a study of coronary heart disease.
Key Words: Bias; Latent variable; Measurement error; Remeasurement method; Simulation extrapolation; Structural modelling
Received January 2005. Revised September 2005.
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