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On latent-variable model misspecification in structural measurement error models for binary response.

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.

SUBMITTER: Huang X

PROVIDER: S-EPMC3229040 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

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On latent-variable model misspecification in structural measurement error models for binary response.

Huang Xianzheng X Tebbs Joshua M JM

Biometrics 20080929 3

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. ...[more]

PMID: 18945265

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