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On Latent‐Variable Model Misspecification in Structural Measurement Error Models for Binary Response
Author(s) -
Huang Xianzheng,
Tebbs Joshua M.
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01128.x
Subject(s) - covariate , estimator , pooling , latent variable , latent variable model , statistics , binary number , observational error , errors in variables models , binary data , computer science , variable (mathematics) , mathematics , econometrics , artificial intelligence , mathematical analysis , arithmetic
Summary We consider structural measurement error models for a binary response. We show that likelihood‐based estimators obtained from fitting structural measurement error models with pooled binary responses can be far more robust to covariate measurement error in the presence of latent‐variable model misspecification than the corresponding estimators from individual responses. Furthermore, despite the loss in information, pooling can provide improved parameter estimators in terms of mean‐squared error. Based on these and other findings, we create a new diagnostic method to detect latent‐variable model misspecification in structural measurement error models with individual binary response. We use simulation and data from the Framingham Heart Study to illustrate our methods.