Robustness in Structural Measurement Error Models

• Activity Number: 364
• Type: Contributed
• Date/Time: Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
• Sponsor: General Methodology
• Author(s): Xianzheng Huang*+ and Leonard A. Stefanski and Marie Davidian
• Companies: North Carolina State University and North Carolina State University and North Carolina State University
• Keywords: Bias ; Latent variable ; Measurement error ; Remeasurement method ; Robust ; Structural modeling

Abstract

Models involving unobservable latent quantities, such as structural measurement error models and so-called ``joint' models for longitudinal and time-to-event data, are widely used in a host of applications. Provided the model for the latent variable is correctly specified, likelihood-based approaches are appealing because they lead to consistent and efficient inference. However, intuition suggests misspecification of this model may compromise such inference, although recent empirical studies have exhibited striking robustness to the assumption on the latent variable. The data analyst faces the difficulty that the extent to which inference may be sensitive to the choice of model for unobservable latent variables is not known in a given problem. Techniques for studying and diagnosing robustness in these models would thus be invaluable. We present a framework for assessing model robustness in the class of structural latent variable models, focusing on the particular subclass of structural measurement error models, and propose practical strategies for diagnosing misspecification of the model for the true predictor, the latent variable for this subclass.