On latent-variable model misspecification in structural measurement error models for binary response
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Published on: September 27, 2019
Xianzheng Huang1, Joshua M Tebbs
1Department of Statistics, University of South Carolina, Columbia, South Carolina 29208, USA. huang@stat.sc.edu
Abstract:
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.
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